Question: Raziel: Confidential and Verifiable Smart Contracts on Blockchain Platforms.

This document details the concept of Oblivious Transfer (OT), a cryptographic technique that allows two parties to exchange secrets without revealing any additional information about their respective secrets. The paper, being a seminal work in the field and often referenced, has been meticulously typeset to ensure fidelity to the original manuscript. Only minor corrections, such as fixing typographical errors and adjusting punctuation, have been made to improve clarity. In keeping with the cryptographic tradition, the roles of Alice and Bob are central to illustrating the protocol. To enhance readability, Alice’s messages are highlighted in red, and Bob’s in blue. The accuracy of this typeset version was thoroughly checked by Y. Sobhdel, a colleague, who provided invaluable feedback. Additionally, H. M. Moghaddam spotted a small error in a previous draft, contributing to the document’s refinement. Any further corrections or feedback are greatly appreciated.

**Question**: A Comparative Study of Machine Learning Techniques for Stock Market Prediction

**Documents**: A machine learning approach to predict stock prices using LSTM networks
In this paper, a machine learning model is proposed for stock price prediction using Long Short-Term Memory (LSTM) networks, a type of recurrent neural network (RNN). The LSTM model is trained on historical stock prices and can predict future stock prices with high accuracy. The model is tested on the S&P 500 stock index and achieves a mean absolute error (MAE) of 0.14 and a root mean squared error (RMSE) of 0.17. The results show that the LSTM model outperforms other machine learning models such as the support vector machine (SVM) and the random forest (RF) in terms of prediction accuracy. The LSTM model is also able to capture the non-linear relationships between stock prices and other financial indicators such as trading volume and market sentiment. The proposed model can be used by investors and traders to make informed decisions about buying and selling stocks.

**Similar Documents**: This paper introduces an advanced machine learning framework for forecasting stock prices, utilizing Long Short-Term Memory (LSTM) networks, a specialized form of recurrent neural networks (RNNs). The LSTM model is meticulously trained on extensive historical stock price data, demonstrating a remarkable ability to predict future stock prices with precision. When applied to the S&P 500 stock index, the model exhibits a mean absolute error (MAE) of 0.14 and a root mean squared error (RMSE) of 0.17, signifying its high

Sharing healthcare data between institutions is challenging. Heterogeneous data structures may preclude compatibility, while disparate use of healthcare terminology limits data comprehension. Even if structure and semantics could be agreed upon, both security and data consistency concerns abound. Centralized data stores and authority providers are attractive targets for cyber attack, and establishing a consistent view of the patient record across a data sharing network is problematic. In this work we present a Blockchain-based approach to sharing patient data. This approach trades a single centralized source of trust in favor of network consensus, and predicates consensus on proof of structural and semantic interoperability.

One of the central challenges for mathematical cryptography is to create a payment system that provides the advantages of cash in a digital world. In this expository article we describe two very different solutions to this problem. The first is an elliptic-curve-based version of a construction of S. Brands, and the second is Bitcoin. We also discuss a generalization of Bitcoin that supports peer-to-peer contracts. AMS subject classifications. 94A60, 68P25, 14G50, 94-02.

In recent years, encrypted databases have emerged as a promising direction that provides data confidentiality without sacrificing functionality: queries are executed on encrypted data. However, existing practical proposals rely on a set of weak encryption schemes that have been shown to leak sensitive data. In this paper, we propose Arx, a practical and functionally rich database system that encrypts the data only with semantically secure encryption schemes. We show that Arx supports real applications such as ShareLaTeX and a health data cloud provider with a modest performance overhead.

The concept of smart grid has emerged as a convergence of traditional power system engineering and information and communication technology. It is vital to the success of next generation of power grid, which is expected to be featuring reliable, efficient, flexible, clean, friendly, and secure characteristics. In this paper, we propose an efficient and privacy-preserving aggregation scheme, named EPPA, for smart grid communications. EPPA uses a superincreasing sequence to structure multidimensional data and encrypt the structured data by the homomorphic Paillier cryptosystem technique. For data communications from user to smart grid operation center, data aggregation is performed directly on ciphertext at local gateways without decryption, and the aggregation result of the original data can be obtained at the operation center. EPPA also adopts the batch verification technique to reduce authentication cost. Through extensive analysis, we demonstrate that EPPA resists various security threats and preserve user privacy, and has significantly less computation and communication overhead than existing competing approaches.

A smart-card-based user authentication scheme for wireless sensor networks (in short, a SUA-WSN scheme) is designed to restrict access to the sensor data only to users who are in possession of both a smart card and the corresponding password. While a significant number of SUA-WSN schemes have been suggested in recent years, their intended security properties lack formal definitions and proofs in a widely-accepted model. One consequence is that SUA-WSN schemes insecure against various attacks have proliferated. In this paper, we devise a security model for the analysis of SUA-WSN schemes by extending the widely-accepted model of Bellare, Pointcheval and Rogaway (2000). Our model provides formal definitions of authenticated key exchange and user anonymity while capturing side-channel attacks, as well as other common attacks. We also propose a new SUA-WSN scheme based on elliptic curve cryptography (ECC), and prove its security properties in our extended model. To the best of our knowledge, our proposed scheme is the first SUA-WSN scheme that provably achieves both authenticated key exchange and user anonymity. Our scheme is also computationally competitive with other ECC-based (non-provably secure) schemes.

This is the second of a series of papers describing the results of a project whose goal was to identify five remote identity proofing solutions that can be used as alternatives to knowledge-based verification. This paper describes the second solution, which makes use of a rich credential adapted for use on a blockchain and backed by a blockchain PKI. A rich credential, also used in Solution 1, allows the subject to identify him/herself to a remote verifier with which the subject has no prior relationship by presenting verification factors including possession of a private key, knowledge of a password, and possession of one or more biometric features, with selective disclosure of attributes and selective presentation of verification factors. In Solution 2 the issuer is a bank and the biometric verification factor is speaker recognition, which can be combined with face recognition to defeat voice morphing. The paper describes in detail the concept of a blockchain PKI, and shows that it has remarkable advantages over a traditional PKI, notably the fact that revocation checking is performed on the verifier’s local copy of the blockchain without requiring CRLs or OCSP.

In the Big Data era, personal metadata may will become a new type of corporate asset, however there have already been a growing public concern about user’s privacy mined from metadata. In this paper we address the problem of implementing the self-sovereignty of personal metadata on the existing OpenPDS/SafeAnswers framework according to the Windhover Principle. In order to do that, we propose a new framework, called BlocakChain-based Personal Data Store (BCPDS), to realize two basic properties: notary and autonomy. This framework, firstly introduces the BlockChain as a notary, into OpenPDS/SafeAnswers for secure storage of personal meta-data instead of the original database. Next, we present an AutoNomybased Access Control (ANAC) to improve the SafeAnswers module, where ANAC is a new mechanism that enforces access based on the relationship among all authorized users and metadata’s owner. In addition, we also propose General Access Structure (GAS) and threshold secret sharing scheme in BlockChain as an implementation method for our BC-PDS framework.

We introduce new theoretical measures for the qualitative and quantitative assessment of encryption schemes designed for broadcast transmissions. The goal is to allow a central broadcast site to broadcast secure transmissions to an arbitrary set of recipients while minimizing key management related transmissions. We present several schemes that allow a center to broadcast a secret to any subset of privileged users out of a universe of size so that coalitions of users not in the privileged set cannot learn the secret. The most interesting scheme requires every user to store keys and the center to broadcast messages regardless of the size of the privileged set. This scheme is resilient to any coalition of users. We also present a scheme that is resilient with probability against a random subset of users. This scheme requires every user to store keys and the center to broadcast messages. Preliminary version appeared in Advances in Cryptology CRYPTO ’93 Proceedings, Lecture Notes in Computer Science, Vol. 773, 1994, pp. 480–491. Dept. of Computer Science, School of Mathematics, Tel Aviv University, Tel Aviv, Israel, and Algorithmic Research Ltd. E-mail fiat@math.tau.ac.il. Incumbent of the Morris and Rose Goldman Career Development Chair, Dept. of Applied Mathematics and Computer Science, Weizmann Institute of Science, Rehovot 76100, Israel. Research supported by an Alon Fellowship and a grant from the Israel Science Foundation administered by the Israeli Academy of Sciences. E-mail: naor@wisdom.weizmann.ac.il.

The blockchain-based digital content distribution system was developed. Decentralized and pear-to-pear authentication mechanism can be considered as the ideal rights management mechanism. The blockchain has the potential to realize this ideal content distribution system. This is the successful model of the Superdistribution concept which was announced almost 30 years ago. The proposed system was demonstrated and got a lot of feedback for the future practical system.

The one-time pad, the mother of all encryption schemes, is well known to be information-theoretically secure, in contrast to most encryption schemes used in practice, which are at most computationally secure. In this paper, we focus on another, completely different aspect in which the one-time pad is superior to normal encryption, and which surfaces only when the receiver (not only the eavesdropper) is considered potentially dishonest, as can be the case in a larger protocol context in which encryption is used as a sub-protocol. For example, such a dishonest receiver (who is, say, coerced by the eavesdropper) can in normal encryption verifiably leak the message to the eavesdropper by revealing the secret key. While this leakage feature can provably not be avoided completely, it is more limited if the one-time pad is used. We use the constructive cryptography framework to make these statements precise.


Question: Raziel: Confidential and Verifiable Smart Contracts on Blockchain Platforms.

This paper delves into the security analysis of the Transport Layer Security (TLS) protocol, focusing on its handshake mechanism, which is crucial for establishing secure connections on the internet. Despite the widespread understanding of individual ciphersuites, the composite nature of the TLS handshake poses significant security challenges, especially considering the practical implications for ensuring robust security guarantees. Through empirical investigation, we uncover that major TLS implementations share key materials across various algorithms, some of which are of questionable strength. This sharing of keys can lead to vulnerabilities, particularly in the context of resumption and renegotiation processes, highlighting the need for a comprehensive security model that considers multiple interrelated instances of the handshake.

To address these issues, we conduct a detailed security analysis of the TLS handshake as implemented and utilized in real-world scenarios. Our study leverages miTLS, a rigorously verified reference implementation of the TLS protocol, which successfully interoperates with mainstream browsers and servers for multiple protocol versions, configurations, and ciphersuites. miTLS not only provides application-level security but also offers provable security guarantees for certain configurations.

We introduce novel agile security definitions and assumptions tailored to the signatures, key encapsulation mechanisms (KEM), and key derivation algorithms employed by the TLS handshake. These definitions are designed to be more robust than those required for simpler, modern protocols, reflecting the complex nature of TLS. We validate our KEM model by proving the compliance of both RSA and Diffie-Hellman ciphersuites with our KEM definition. Notably, we formalize the use of PKCS#1v1.5 encryption within TLS, incorporating recommended countermeasures against Bleichenbacher attacks, and develop an extensive EasyCrypt proof demonstrating the security of the resulting master secret KEM against replayable chosen-ciphertext attacks. This proof assumes that ciphertexts are difficult to re-randomize.

Building upon our agile definitions, we establish a modular proof of security for the miTLS reference implementation of the TLS handshake, encompassing ciphersuite negotiation, key exchange, renegotiation, and resumption. This proof is based on a detailed 3,600-line executable model. We present our core definitions, constructions, and proofs for an abstract protocol model, involving a series of related TLS handshake runs with diverse ciphersuites. Additionally, we outline the refinement process applied to the entire reference implementation, facilitated by automated verification tools.

Modern web services rob users of low-level control over cloud storage; a user’s single logical data set is scattered across multiple storage silos whose access controls are set by the web services, not users. The result is that users lack the ultimate authority to determine how their data is shared with other web services. In this thesis, we introduce Sieve, a new architecture for selectively exposing user data to third party web services in a provably secure manner. Sieve starts with a user-centric storage model: each user uploads encrypted data to a single cloud store, and by default, only the user knows the decryption keys. Given this storage model, Sieve defines an infrastructure to support rich, legacy web applications. Using attribute-based encryption, Sieve allows users to define intuitive, understandable access policies that are cryptographically enforceable. Using key homomorphism, Sieve can re-encrypt user data on storage providers in situ, revoking decryption keys from web services without revealing new ones to the storage provider. Using secret sharing and two-factor authentication, Sieve protects against the loss of user devices like smartphones and laptops. The result is that users can enjoy rich, legacy web applications, while benefitting from cryptographically strong controls over what data the services can access. Thesis Supervisor: Nickolai Zeldovich Title: Associate Professor Thesis Supervisor: James Mickens Title: Associate Professor

The KeY system offers a platform of software analysis tools for sequential Java. Foremost, this includes full functional verification against contracts written in the Java Modeling Language. But the approach is general enough to provide a basis for other methods and purposes: (i) complementary validation techniques to formal verification such as testing and debugging, (ii) methods that reduce the complexity of verification such as modularization and abstract interpretation, (iii) analyses of non-functional properties such as information flow security, and (iv) sound program transformation and code generation. We show that deductive technology that has been developed for full functional verification can be used as a basis and framework for other purposes than pure functional verification. We use the current release of the KeY system as an example to explain and prove this claim.

The blockchain initially gained traction in 2008 as the technology underlying Bitcoin [105], but now has been employed in a diverse range of applications and created a global market worth over $150B as of 2017. What distinguishes blockchains from traditional distributed databases is the ability to operate in a decentralized setting without relying on a trusted third party. As such their core technical component is consensus: how to reach agreement among a group of nodes. This has been extensively studied already in the distributed systems community for closed systems, but its application to open blockchains has revitalized the field and led to a plethora of new designs. The inherent complexity of consensus protocols and their rapid and dramatic evolution makes it hard to contextualize the design landscape. We address this challenge by conducting a systematic and comprehensive study of blockchain consensus protocols. After first discussing key themes in classical consensus protocols, we describe: (i) protocols based on proof-of-work (PoW), (ii) proof-of-X (PoX) protocols that replace PoW with more energy-efficient alternatives, and (iii) hybrid protocols that are compositions or variations of classical consensus protocols. We develop a framework to evaluate their performance, security and design properties, and use it to systematize key themes in the protocol categories described above. This evaluation leads us to identify research gaps and challenges for the community to consider in future research endeavours.

In a proof-of-retrievability system, a data storage center must prove to a verifier that he is actually storing all of a client’s data. The central challenge is to build systems that are both efficient and provably secure—that is, it should be possible to extract the client’s data from any prover that passes a verification check. In this paper, we give the first proof-of-retrievability schemes with full proofs of security against arbitrary adversaries in the strongest model, that of Juels and Kaliski. Our first scheme, built from BLS signatures and secure in the random oracle model, features a proof-of-retrievability protocol in which the client’s query and server’s response are both extremely short. This scheme allows public verifiability: anyone can act as a verifier, not just the file owner. Our second scheme, which builds on pseudorandom functions (PRFs) and is secure in the standard model, allows only private verification. It features a proof-of-retrievability protocol with an even shorter server’s response than our first scheme, but the client’s query is long. Both schemes rely on homomorphic properties to aggregate a proof into one small authenticator value.

Secure search techniques over encrypted cloud data allow an authorized user to query data files of interest by submitting encrypted query keywords to the cloud server in a privacy-preserving manner. However, in practice, the returned query results may be incorrect or incomplete in the dishonest cloud environment. For example, the cloud server may intentionally omit some qualified results to save computational resources and communication overhead. Thus, a well-functioning secure query system should provide a query results verification mechanism that allows the data user to verify results. In this paper, we design a secure, easily integrated, and fine-grained query results verification mechanism, by which, given an encrypted query results set, the query user not only can verify the correctness of each data file in the set but also can further check how many or which qualified data files are not returned if the set is incomplete before decryption. The verification scheme is loose-coupling to concrete secure search techniques and can be very easily integrated into any secure query scheme. We achieve the goal by constructing secure verification object for encrypted cloud data. Furthermore, a short signature technique with extremely small storage cost is proposed to guarantee the authenticity of verification object and a verification object request technique is presented to allow the query user to securely obtain the desired verification object. Performance evaluation shows that the proposed schemes are practical and efficient.

Cloud-based multimedia systems are becoming increasingly common. These systems offer not only storage facility, but also high-end computing infrastructure which can be used to process data for various analysis tasks ranging from low-level data quality enhancement to high-level activity and behavior identification operations. However, cloud data centers, being third party servers, are often prone to information leakage, raising security and privacy concerns. In this article, we present a Shamir's secret sharing based method to enhance the quality of encrypted image data over cloud. Using the proposed method we show that several image enhancement operations such as noise removal, antialiasing, edge and contrast enhancement, and dehazing can be performed in encrypted domain with near-zero loss in accuracy and minimal computation and data overhead. Moreover, the proposed method is proven to be information theoretically secure.

We design and build ObliviStore, a high performance, distributed ORAM-based cloud data store secure in the malicious model. To the best of our knowledge, ObliviStore is the fastest ORAM implementation known to date, and is faster by 10X or more in comparison with the best known ORAM implementation. ObliviStore achieves high throughput by making I/O operations asynchronous. Asynchrony introduces security challenges, i.e., we must prevent information leakage not only through access patterns, but also through timing of I/O events. We propose various practical optimizations which are key to achieving high performance, as well as techniques for a data center to dynamically scale up a distributed ORAM. We show that with 11 trusted machines (each with a modern CPU), and 20 Solid State Drives, ObliviStore achieves a throughput of 31.5MB/s with a block size of 4KB.

Today, isolated trusted computation and code execution is of paramount importance to protect sensitive information and workfl ows from other malicious privileged or unprivileged software. Intel Software Guard Extensions (SGX) is a set of security architecture extensions fi rst introduced in the Skylake microarchitecture that enables a Trusted Execution Environment (TEE). It provides an ‘inverse sandbox’, for sensitive programs, and guarantees the integrity and confi dentiality of secure computations, even from the most privileged malicious software (e.g. OS, hypervisor). SGX-capable CPUs only became available in production systems in Q3 2015, and they are not yet fully supported and adopted in systems. Besides the capability in the CPU, the BIOS also needs to provide support for the enclaves, and not many vendors have released the required updates for the system support. This has led to many wrong assumptions being made about the capabilities, features, and ultimately dangers of secure enclaves. By having access to resources and publications such as white papers, patents and the actual SGX-capable hardware and software development environment, we are in a privileged position to be able to investigate and demystify SGX. In this paper, we fi rst review the previous trusted execution technologies, such as ARM Trust Zone and Intel TXT, to better understand and appreciate the new innovations of SGX. Then, we look at the details of SGX technology, cryptographic primitives and the underlying concepts that power it, namely the sealing, attestation, and the Memory Encryption Engine (MEE). We also consider use cases such as trusted and secure code execution on an untrusted cloud platform, and digital rights management (DRM). This is followed by an overview of the software development environment and the available libraries.

Bit coin has emerged as the most successful cryptographic currency in history. Within two years of its quiet launch in 2009, Bit coin grew to comprise billions of dollars of economic value despite only cursory analysis of the system's design. Since then a growing literature has identified hidden-but-important properties of the system, discovered attacks, proposed promising alternatives, and singled out difficult future challenges. Meanwhile a large and vibrant open-source community has proposed and deployed numerous modifications and extensions. We provide the first systematic exposition Bit coin and the many related crypto currencies or 'altcoins.' Drawing from a scattered body of knowledge, we identify three key components of Bit coin's design that can be decoupled. This enables a more insightful analysis of Bit coin's properties and future stability. We map the design space for numerous proposed modifications, providing comparative analyses for alternative consensus mechanisms, currency allocation mechanisms, computational puzzles, and key management tools. We survey anonymity issues in Bit coin and provide an evaluation framework for analyzing a variety of privacy-enhancing proposals. Finally we provide new insights on what we term disinter mediation protocols, which absolve the need for trusted intermediaries in an interesting set of applications. We identify three general disinter mediation strategies and provide a detailed comparison.

Zero-knowledge proofs of knowledge and fully-homomorphic encryption are two areas that have seen considerable advances in recent years, and these two techniques are used in conjunction in the context of verifiable decryption. Existing solutions for verifiable decryption are aimed at the batch setting, however there are many applications in which there will only be one ciphertext that requires a proof of decryption. The purpose of this paper is to provide a zero-knowledge proof of correct decryption on an FHE ciphertext, which for instance could hold the result of a cryptographic election. We give two main contributions. Firstly, we present a bootstrapping-like protocol to switch from one FHE scheme to another. The first scheme has efficient homomorphic capabilities; the second admits a simple zero-knowledge protocol. To illustrate this, we use the Brakerski et al. (ITCS, 2012) scheme for the former, and Gentry’s original scheme (STOC, 2009) for the latter. Secondly, we present a simple one-shot zero-knowledge protocol for verifiable decryption using Gentry’s original FHE scheme.


Question: Raziel: Confidential and Verifiable Smart Contracts on Blockchain Platforms

This paper introduces an innovative framework for enhancing secure multi-party computation (MPC) protocols by enabling them to operate effectively even in scenarios without an honest majority. The proposed method, which we refer to as the OT-efficient compiler, transforms MPC protocols that require an honest majority into protocols that maintain security in the absence of such a majority, by leveraging the oblivious transfer (OT) hybrid model. Our approach is straightforward yet powerful, involving the strategic combination of a secure protocol designed for honest majority settings with another protocol that ensures security against semi-honest adversaries in environments lacking an honest majority. By applying this compiler to various protocols from the literature, we achieve several significant advancements in the field of secure two-party computation and MPC without an honest majority. These include:

- Achieving constant-rate two-party computation in the OT-hybrid model. We devise a statistically universally composable (UC) secure two-party protocol within the OT-hybrid model that can evaluate a circuit C of size s and depth d with a total communication complexity of O(s) + poly(k, d, log s) and O(d) rounds. This result extends to a constant number of parties, demonstrating its scalability.

- Efficient OT extension in the malicious model. Our method yields a computationally efficient protocol for generating numerous string OTs from a limited number of string OTs, achieving a constant amortized communication overhead relative to the total length of the string OTs.

- Black-box constructions for constant-round MPC without an honest majority. We construct general computationally UC-secure MPC protocols in the OT-hybrid model that require only a constant number of rounds and employ black-box access to a pseudorandom generator. This marks the first time constant-round protocols for three or more parties have been developed that utilize black-box cryptographic primitives, avoiding the need for resource-intensive zero-knowledge proofs.

Our OT-efficient compiler represents a significant step forward in the field of secure computation, offering practical solutions for enhancing the robustness and efficiency of MPC protocols in challenging environments.

The KeY system offers a platform of software analysis tools for sequential Java. Foremost, this includes full functional verification against contracts written in the Java Modeling Language. But the approach is general enough to provide a basis for other methods and purposes: (i) complementary validation techniques to formal verification such as testing and debugging, (ii) methods that reduce the complexity of verification such as modularization and abstract interpretation, (iii) analyses of non-functional properties such as information flow security, and (iv) sound program transformation and code generation. We show that deductive technology that has been developed for full functional verification can be used as a basis and framework for other purposes than pure functional verification. We use the current release of the KeY system as an example to explain and prove this claim.

There have been several attempts recently at using homomorphic encryption to increase the efficiency of Oblivious RAM protocols. One of the most successful has been Onion ORAM, which achieves O(1) communication overhead with polylogarithmic server computation. However, it has two drawbacks. It requires a large block size of B = Ω(log6 N) with large constants. Moreover, while it only needs polylogarithmic computation complexity, that computation consists mostly of expensive homomorphic multiplications. In this work, we address these problems and reduce the required block size to Ω(log4 N). We remove most of the homomorphic multiplications while maintaining O(1) communication complexity. Our idea is to replace their homomorphic eviction routine with a new, much cheaper permute-and-merge eviction which eliminates homomorphic multiplications and maintains the same level of security. In turn, this removes the need for layered encryption that Onion ORAM relies on and reduces both the minimum block size and server computation.

Today, isolated trusted computation and code execution is of paramount importance to protect sensitive information and workfl ows from other malicious privileged or unprivileged software. Intel Software Guard Extensions (SGX) is a set of security architecture extensions fi rst introduced in the Skylake microarchitecture that enables a Trusted Execution Environment (TEE). It provides an ‘inverse sandbox’, for sensitive programs, and guarantees the integrity and confi dentiality of secure computations, even from the most privileged malicious software (e.g. OS, hypervisor). SGX-capable CPUs only became available in production systems in Q3 2015, and they are not yet fully supported and adopted in systems. Besides the capability in the CPU, the BIOS also needs to provide support for the enclaves, and not many vendors have released the required updates for the system support. This has led to many wrong assumptions being made about the capabilities, features, and ultimately dangers of secure enclaves. By having access to resources and publications such as white papers, patents and the actual SGX-capable hardware and software development environment, we are in a privileged position to be able to investigate and demystify SGX. In this paper, we fi rst review the previous trusted execution technologies, such as ARM Trust Zone and Intel TXT, to better understand and appreciate the new innovations of SGX. Then, we look at the details of SGX technology, cryptographic primitives and the underlying concepts that power it, namely the sealing, attestation, and the Memory Encryption Engine (MEE). We also consider use cases such as trusted and secure code execution on an untrusted cloud platform, and digital rights management (DRM). This is followed by an overview of the software development environment and the available libraries.

Cloud-based multimedia systems are becoming increasingly common. These systems offer not only storage facility, but also high-end computing infrastructure which can be used to process data for various analysis tasks ranging from low-level data quality enhancement to high-level activity and behavior identification operations. However, cloud data centers, being third party servers, are often prone to information leakage, raising security and privacy concerns. In this article, we present a Shamir's secret sharing based method to enhance the quality of encrypted image data over cloud. Using the proposed method we show that several image enhancement operations such as noise removal, antialiasing, edge and contrast enhancement, and dehazing can be performed in encrypted domain with near-zero loss in accuracy and minimal computation and data overhead. Moreover, the proposed method is proven to be information theoretically secure.

In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.
 A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.
 In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.
 We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

Cryptocurrencies like Bitcoin have proven to be a phenomenal success. Bitcoin-like systems use proofof-work mechanism which is therefore considered as 1-hop blockchain, and their security holds if the majority of the computing power is under the control of honest players. However, this assumption has been seriously challenged recently and Bitcoin-like systems will fail when this assumption is broken. We propose the rst provably secure 2-hop blockchain by combining proof-of-work (rst hop) and proof-of-stake (second hop) mechanisms. On top of Bitcoin’s brilliant ideas of utilizing the power of the honest miners, via their computing resources, to secure the blockchain, we further leverage the power of the honest users/stakeholders, via their coins/stake, to achieve this goal. e security of our blockchain holds if the honest players control majority of the collective resources (which consists of both computing power and stake). at said, even if the adversary controls more than 50% computing power, the honest players still have the chance to defend the blockchain via honest stake. ∗An early version with title “Securing Bitcoin-like Blockchains against a Malicious Majority of Computing Power” appeared in ePrint Archive in July 2016. e current version shares the same motivation. But the construction idea and modeling approach have been completely revised. †Virginia Commonwealth University. Email: duong3@vcu.edu. ‡Shanghai Jiao Tong University. Most work done while visiting Cryptography Lab at Virginia Commonwealth University. Email: fanlei@sjtu.edu.cn. §Virginia Commonwealth University. Email: hszhou@vcu.edu.

We study the setting in which a user stores encrypted documents (e.g. e-mails) on an untrusted server. In order to retrieve documents satisfying a certain search criterion, the user gives the server a capability that allows the server to identify exactly those documents. Work in this area has largely focused on search criteria consisting of a single keyword. If the user is actually interested in documents containing each of several keywords (conjunctive keyword search) the user must either give the server capabilities for each of the keywords individually and rely on an intersection calculation (by either the server or the user) to determine the correct set of documents, or alternatively, the user may store additional information on the server to facilitate such searches. Neither solution is desirable; the former enables the server to learn which documents match each individual keyword of the conjunctive search and the latter results in exponential storage if the user allows for searches on every set of keywords. We define a security model for conjunctive keyword search over encrypted data and present the first schemes for conducting such searches securely. We propose first a scheme for which the communication cost is linear in the number of documents, but that cost can be incurred “offline” before the conjunctive query is asked. The security of this scheme relies on the Decisional Diffie-Hellman (DDH) assumption. We propose a second scheme whose communication cost is on the order of the number of keyword fields and whose security relies on a new hardness assumption.

In a proof-of-retrievability system, a data storage center must prove to a verifier that he is actually storing all of a client’s data. The central challenge is to build systems that are both efficient and provably secure—that is, it should be possible to extract the client’s data from any prover that passes a verification check. In this paper, we give the first proof-of-retrievability schemes with full proofs of security against arbitrary adversaries in the strongest model, that of Juels and Kaliski. Our first scheme, built from BLS signatures and secure in the random oracle model, features a proof-of-retrievability protocol in which the client’s query and server’s response are both extremely short. This scheme allows public verifiability: anyone can act as a verifier, not just the file owner. Our second scheme, which builds on pseudorandom functions (PRFs) and is secure in the standard model, allows only private verification. It features a proof-of-retrievability protocol with an even shorter server’s response than our first scheme, but the client’s query is long. Both schemes rely on homomorphic properties to aggregate a proof into one small authenticator value.

It is desirable to store data on data storage servers such as mail servers and file servers in encrypted form to reduce security and privacy risks. But this usually implies that on e has to sacrifice functionality for security. For example, if a client wishes to retrieve only documents containing certai n words, it was not previously known how to let the data storage server perform the search and answer the query without loss of data confidentiality. In this paper, we describe our cryptographic schemes for the problem of searching on encrypted data and provide proofs of security for the resulting crypto systems. Ou r techniques have a number of crucial advantages. They are provably secure : they provideprovable secrecy for encryption, in the sense that the untrusted server cannot learn anything about the plaintext when only given the ciphertext; they providequery isolationfor searches, meaning that the untrusted server cannot learn anything more about the plaintext than the search result; they provide controlled searching, so that the untrusted server cannot search for an arbitrary word without the user’s authorization; they also supporthidden queries , so that the user may ask the untrusted server to search for a secret word without revealing the word to the server. The algorithms we present are simple, fast (for a document of length n, the encryption and search algorithms only need O(n) stream cipher and block cipher operations), and introduce almost no space and communication overhead, and hence are practical to use today. We gratefully acknowledge support for this research from se veral US government agencies. This research was suported in part by t he Defense Advanced Research Projects Agency under DARPA contract N66 01-9928913 (under supervision of the Space and Naval Warfare Syst ems Center San Diego), by the National Science foundation under grant F D99-79852, and by the United States Postal Service under grant USPS 1025 90-98-C3513. Views and conclusions contained in this document are t hose of the authors and do not necessarily represent the official opinio n or policies, either expressed or implied of the US government or any of its agencies, DARPA, NSF, USPS.

Intel Software Guard extensions (SGX) enable secure and trusted execution of user code in an isolated enclave to protect against a powerful adversary. Unfortunately, running I/O-intensive, memory-demanding server applications in enclaves leads to significant performance degradation. Such applications put a substantial load on the in-enclave system call and secure paging mechanisms, which turn out to be the main reason for the application slowdown. In addition to the high direct cost of thousands-of-cycles long SGX management instructions, these mechanisms incur the high indirect cost of enclave exits due to associated TLB flushes and processor state pollution.
 We tackle these performance issues in Eleos by enabling exit-less system calls and exit-less paging in enclaves. Eleos introduces a novel Secure User-managed Virtual Memory (SUVM) abstraction that implements application-level paging inside the enclave. SUVM eliminates the overheads of enclave exits due to paging, and enables new optimizations such as sub-page granularity of accesses.
 We thoroughly evaluate Eleos on a range of microbenchmarks and two real server applications, achieving notable system performance gains. memcached and a face verification server running in-enclave with Eleos, achieves up to 2.2× and 2.3× higher throughput respectively while working on datasets up to 5× larger than the enclave's secure physical memory.


Question: Raziel: Ensuring privacy and verifiability in smart contracts on blockchain platforms.

This paper presents an improved analysis of the concrete security and key sizes for lattice-based encryption schemes rooted in the "learning with errors" (LWE) problem. Our key advancements include: (1) the introduction of an enhanced lattice attack strategy on LWE that integrates basis reduction with an enumeration algorithm, offering a flexible time/success tradeoff, which outperforms the conventional distinguishing attack previously used in security evaluations; (2) the proposal of concrete parameters and security estimations for a novel LWE-based cryptosystem that offers superior compactness and efficiency compared to established schemes in the field. The proposed key sizes are notably smaller, up to 10 times more compact than previous benchmarks, while simultaneously ensuring higher concrete security levels.

**Question**: A Comparative Study of Deep Learning Architectures for Image Recognition

**Documents**: Deep Residual Learning for Image Recognition
We propose a deep residual learning framework to ease the training of networks that are substantially deeper than those used previously. With this new framework, we are able to train a residual network with 152 layers - 8x deeper than VGG nets - but still have lower complexity. The framework can also be used to train an ensemble of very deep networks without much additional computational cost. Our training strategy for these residual networks is not substantially different from standard networks. Residual learning is easy to optimize and yields better generalization. With residual networks, we won the 1st place on the ILSVRC 2015 classification task by a significant margin over the 2nd place.

**Similar Documents**: This paper introduces a deep residual learning framework designed to facilitate the training of networks with significantly greater depth than previously attainable. Utilizing this framework, we successfully train a residual network with 152 layers, an 8-fold increase in depth compared to VGG nets, while maintaining lower computational complexity. The framework's versatility enables the efficient training of ensembles of very deep networks with minimal additional computational overhead. Our training approach for residual networks is largely consistent with that of standard networks. Residual learning exhibits ease of optimization and superior generalization capabilities. The application of residual networks led to our victory in the ILSVRC 2015 classification task, where we secured the 1st place with a substantial lead over the 2nd place competitor.

Companies require data from multiple sources to develop new information systems, such as social networking, e-commerce and location-based services. Systems rely on complex, multi-stakeholder data supply-chains to deliver value. These data supply-chains have complex privacy requirements: privacy policies affecting multiple stakeholders (e.g. user, developer, company, government) regulate the collection, use and sharing of data over multiple jurisdictions (e.g. California, United States, Europe). Increasingly, regulators expect companies to ensure consistency between company privacy policies and company data practices. To address this problem, we propose a methodology to map policy requirements in natural language to a formal representation in Description Logic. Using the formal representation, we reason about conflicting requirements within a single policy and among multiple policies in a data supply chain. Further, we enable tracing data flows within the supply-chain. We derive our methodology from an exploratory case study of Facebook platform policy. We demonstrate the feasibility of our approach in an evaluation involving Facebook, Zynga and AOL-Advertising policies. Our results identify three conflicts that exist between Facebook and Zynga policies, and one conflict within the AOL Advertising policy.

Along with the rapid advancement of digital image processing technology, image denoising remains a fundamental task, which aims to recover the original image from its noisy observation. With the explosive growth of images on the Internet, one recent trend is to seek high quality similar patches at cloud image databases and harness rich redundancy therein for promising denoising performance. Despite the well-understood benefits, such a cloud-based denoising paradigm would undesirably raise security and privacy issues, especially for privacy-sensitive image data sets. In this paper, we initiate the first endeavor toward privacy-preserving image denoising from external cloud databases. Our design enables the cloud hosting encrypted databases to provide secure query-based image denoising services. Considering that image denoising intrinsically demands high quality similar image patches, our design builds upon recent advancements on secure similarity search, Yao’s garbled circuits, and image denoising operations, where each is used at a different phase of the design for the best performance. We formally analyze the security strengths. Extensive experiments over real-world data sets demonstrate that our design achieves the denoising quality close to the optimal performance in plaintext.

Privacy-preserving data aggregation in ad hoc networks is a challenging problem, considering the distributed communication and control requirement, dynamic network topology, unreliable communication links, etc. The difficulty is exaggerated when there exist dishonest nodes, and how to ensure privacy, accuracy, and robustness against dishonest nodes remains a n open issue. Different from the widely used cryptographic approaches, in this paper, we address this challenging proble m by exploiting the distributed consensus technique. We first pr opose a secure consensus-based data aggregation (SCDA) algorith m that guarantees an accurate sum aggregation while preservi ng the privacy of sensitive data. Then, to mitigate the polluti on from dishonest nodes, we propose an Enhanced SCDA (E-SCDA) algorithm that allows neighbors to detect dishonest nodes, and derive the error bound when there are undetectable dishones t nodes. We prove the convergence of both SCDA and E-SCDA. We also prove that the proposed algorithms are(ǫ, σ)-dataprivacy, and obtain the mathematical relationship betweenǫ and σ. Extensive simulations have shown that the proposed algori thms have high accuracy and low complexity, and they are robust against network dynamics and dishonest nodes.

Rich client-side applications written in HTML5 proliferate diverse platforms such as mobile devices, commodity PCs, and the web platform. These client-side HTML5 applications are increasingly accessing sensitive data, including users’ personal and social data, sensor data, and capability-bearing tokens. To fulfill their security and privacy guarantees, these applications need to maintain certain data-confinement invariants. These invariants are not explicitly stated in today’s HTML5 applications and are enforced using ad-hoc mechanisms. The complexity of web applications, coupled with hard-to-analyze client-side languages, leads to low-assurance data-confinement mechanisms in which the whole application needs to be in the TCB to ensure data-confinement in-

We analyse the performance of several clustering algorithms in the digital peerto-peer currency Bitcoin. Clustering in Bitcoin refers to the task of finding addresses that belongs to the same wallet as a given address. In order to assess the effectiveness of clustering strategies we exploit a vulnerability in the implementation of Connection Bloom Filtering to capture ground truth data about 37,585 Bitcoin wallets and the addresses they own. In addition to well-known clustering techniques, we introduce two new strategies, apply them on addresses of the collected wallets and evaluate precision and recall using the ground truth. Due to the nature of the Connection Bloom Filtering vulnerability the data we collect is not without errors. We present a method to correct the performance metrics in the presence of such inaccuracies. Our results demonstrate that even modern wallet software can not protect its users properly. Even with the most basic clustering technique known as multiinput heuristic, an adversary can guess on average 68.59% addresses of a victim. We show that this metric can be further improved by combining several more sophisticated heuristics.

Modern web services rob users of low-level control over cloud storage; a user’s single logical data set is scattered across multiple storage silos whose access controls are set by the web services, not users. The result is that users lack the ultimate authority to determine how their data is shared with other web services. In this thesis, we introduce Sieve, a new architecture for selectively exposing user data to third party web services in a provably secure manner. Sieve starts with a user-centric storage model: each user uploads encrypted data to a single cloud store, and by default, only the user knows the decryption keys. Given this storage model, Sieve defines an infrastructure to support rich, legacy web applications. Using attribute-based encryption, Sieve allows users to define intuitive, understandable access policies that are cryptographically enforceable. Using key homomorphism, Sieve can re-encrypt user data on storage providers in situ, revoking decryption keys from web services without revealing new ones to the storage provider. Using secret sharing and two-factor authentication, Sieve protects against the loss of user devices like smartphones and laptops. The result is that users can enjoy rich, legacy web applications, while benefitting from cryptographically strong controls over what data the services can access. Thesis Supervisor: Nickolai Zeldovich Title: Associate Professor Thesis Supervisor: James Mickens Title: Associate Professor

Many resource allocation problems can be formulated as an optimization problem whose constraints contain sensitive information about participating users. This paper concerns a class of resource allocation problems whose objective function depends on the aggregate allocation (i.e., the sum of individual allocations); in particular, we investigate distributed algorithmic solutions that preserve the privacy of participating users. Without privacy considerations, existing distributed algorithms normally consist of a central entity computing and broadcasting certain public coordination signals to participating users. However, the coordination signals often depend on user information, so that an adversary who has access to the coordination signals can potentially decode information on individual users and put user privacy at risk. We present a distributed optimization algorithm that preserves differential privacy, which is a strong notion that guarantees user privacy regardless of any auxiliary information an adversary may have. The algorithm achieves privacy by perturbing the public signals with additive noise, whose magnitude is determined by the sensitivity of the projection operation onto user-specified constraints. By viewing the differentially private algorithm as an implementation of stochastic gradient descent, we are able to derive a bound for the suboptimality of the algorithm. We illustrate the implementation of our algorithm via a case study of electric vehicle charging. Specifically, we derive the sensitivity and present numerical simulations for the algorithm. Through numerical simulations, we are able to investigate various aspects of the algorithm when being used in practice, including the choice of step size, number of iterations, and the trade-off between privacy level and suboptimality.

Distributed cryptographic ledgers, such as the blockchain, are now being used in recordkeeping. However, they lack a key feature of more traditional recordkeeping systems needed to establish the authenticity of records and enable reliance on them for trustworthy recordkeeping. The missing feature is known in archival science as the archival bond – the mutual relationship that exists among documents by virtue of the actions in which they participate. In this paper, we propose a novel data model and syntax using core web principles that can be used to address this shortcoming in distributed ledgers as recordkeeping systems.

Bitcoin, the arguably most popular cryptocurrency to date, allows users to perform transactions using freely chosen pseudonymous addresses. Previous research, however, suggests that these pseudonyms can easily be linked, implying a lower level of privacy than originally expected. To obfuscate the links between pseudonyms, different mixing methods have been proposed. One of the first approaches is the CoinJoin concept, where multiple users merge their transactions into one larger transaction. In theory, CoinJoin can be used to mix and transact bitcoins simultaneously, in one step. Yet, it is expected that differing bitcoin amounts would allow an attacker to derive the original single transactions. Solutions based on CoinJoin therefore prescribe the use of fixed bitcoin amounts and cannot be used to perform arbitrary transactions.In this paper, we define a model for CoinJoin transactions and metrics that allow conclusions about the provided anonymity. We generate and analyze CoinJoin transactions and show that with differing, representative amounts they generally do not provide any significant anonymity gains. As a solution to this problem, we present an output splitting approach that introduces sufficient ambiguity to effectively prevent linking in CoinJoin transactions. Furthermore, we discuss how this approach could be used in Bitcoin today.

This paper takes an initial step forward in bringing to life the certification mechanisms according to Art. 42 of the General Data Protection Regulation (GDPR). These newly established methods of legal specification act not only as a central vehicle for overcoming widely articulated and discussed legal challenges, but also as a sandbox for the much needed close collaboration between computer sciences and legal studies. In order to illustrate, for example, what data protection seals could look like in the future, the authors propose a methodology for "translating" legal requirements into technical guidelines: architectural blueprints designed using legal requirements. The purpose of these blueprints is to show developers how their solutions might comply with the principle of Privacy by Design (Art. 25 GDPR). To demonstrate this methodology, the authors propose an architectural blueprint that embodies the legal concept of the data subject’s consent (Art. 6 sec. 1 lit. a GDPR) and elevates best practice to a high standard of Privacy by Design. Finally, the authors highlight further legal problems concerning blockchain technology under the GDPR that will have to be addressed in order to achieve a comprehensive certification mechanism for Privacy by Blockchain Design in the future. ACM Classification


Question: An overview of artificial intelligence applications in cognitive radio systems.

An Exploration of Genetic Algorithms for Optimization and Learning

This comprehensive guide presents, in an accessible and tutorial format, the computational methodologies, mathematical instruments, and research outcomes that facilitate students and professionals in harnessing genetic algorithms for problem-solving across various domains. The text integrates computer science techniques, mathematical tools, and empirical findings from research to provide a solid foundation for utilizing genetic algorithms effectively in search, optimization, and machine learning contexts. By combining theoretical insights with practical applications, the book aims to equip readers with the skills necessary to tackle complex problems using genetic algorithms.

In this paper, we discuss the application of machine learning techniques for performance prediction problems in wireless networks. These problems often involve using existing measurement data to predict network performance where direct measurements are not available. We explore the performance of existing machine learning algorithms for these problems and propose a simple taxonomy of main problem categories. As an example, we use an extensive real-world drive test data set to show that classical machine learning methods such as Gaussian process regression, exponential smoothing of time series, and random forests can yield excellent prediction results. Applying these methods to the management of wireless mobile networks has the potential to significantly reduce operational costs while simultaneously improving user experience. We also discuss key challenges for future work, especially with the focus on practical deployment of machine learning techniques for performance prediction in mobile wireless networks.

—constructing intelligent machines, whether or not these operate in the same way as people do; —formalizing knowledge and mechanizing reasoning, both commonsense and refined expertise, in all areas of human endeavor; —using computational models to understand the psychology and behavior of people, animals, and artificial agents; and —making working with computers as easy and as helpful as working with skilled, cooperative, and possibly expert people.

As a component of the intelligent transportation system (ITS) and one of the concrete applications of mobile ad hoc networks, inter-vehicle communication (IVC) has attracted research attention from both the academia and industry of, notably, US, EU, and Japan. The most important feature of IVC is its ability to extend the horizon of drivers and on-board devices (e.g., radar or sensors) and, thus, to improve road traffic safety and efficiency. This paper surveys IVC with respect to key enabling technologies ranging from physical radio frequency to group communication primitives and security issues. The mobility models used to evaluate the feasibility of these technologies are also briefly described. We focus on the discussion of various MAC protocols that seem to be indispensable components in the network protocol stack of IVC. By analyzing the application requirements and the protocols built upon the MAC layer to meet these requirements, we also advocate our perspective that ad hoc routing protocols and group communication primitives migrated from wired networks might not be an efficient way to support the envisioned applications, and that new coordination algorithms directly based on MAC could be designed for this purpose.

Automated intelligent agents inhabiting a shared environment must coordinate their activities Cooperation not merely coordination may improve the performance of the individual agents or the overall behavior of the system they form Research in Distributed Arti cial Intelligence DAI addresses the problem of designing automated intelligent systems which interact e ectively DAI is not the only eld to take on the challenge of understanding cooperation and coordination There are a variety of other multi entity environments in which the entities coordinate their activity and cooperate Among them are groups of people animals particles and computers We argue that in order to address the challenge of building coordinated and collaborated intelligent agents it is bene cial to combine AI techniques with methods and techniques from a range of multi entity elds such as game theory operations research physics and philosophy To support this claim we describe some of our projects where we have successfully taken an interdisciplinary approach We demonstrate the bene ts in applying multi entity methodologies and show the adaptations modi cations and extensions necessary for solving the DAI problems This is an extended version of a lecture presented upon receipt of the Computers and Thought Award at the th International Joint Conference on Arti cial Intelligence in Montreal Canada August

In wireless environments, transmission and reception costs dominate system power consumption, motivating research effort on new technologies capable of reducing the footprint of the radio, paving the way for the Internet of Things. The most important challenge is to reduce power consumption when receivers are idle, the so called idle-listening cost. One approach proposes switching off the main receiver, then introduces new wake-up circuitry capable of detecting an incoming transmission, optionally discriminating the packet destination using addressing, then switching on the main radio only when required. This wake-up receiver technology represents the ultimate frontier in low power radio communication. In this paper, we present a comprehensive literature review of the research progress in wake-up radio (WuR) hardware and relevant networking software. First, we present an overview of the WuR system architecture, including challenges to hardware design and a comparison of solutions presented throughout the last decade. Next, we present various medium access control and routing protocols as well as diverse ways to exploit WuRs, both as an extension of pre-existing systems and as a new concept to manage low-power networking.

The use of flying platforms such as unmanned aerial vehicles (UAVs), popularly known as drones, is rapidly growing in a wide range of wireless networking applications. In particular, with their inherent attributes such as mobility, flexibility, and adaptive altitude, UAVs admit several key potential applications in wireless systems. On the one hand, UAVs can be used as aerial base stations to enhance coverage, capacity, reliability, and energy efficiency of wireless networks. For instance, UAVs can be deployed to complement existing cellular systems by providing additional capacity to hotspot areas as well as to provide network coverage in emergency and public safety situations. On the other hand, UAVs can operate as flying mobile terminals within the cellular networks. Such cellular-connected UAVs can enable a wide range of key applications expanding from real-time video streaming to item delivery. Despite the several benefits and practical applications of using UAVs as aerial wireless devices, one must address many technical challenges. In this paper, a comprehensive tutorial on the potential benefits and applications of UAVs in wireless communications is presented. Moreover, the important challenges and the fundamental tradeoffs in UAV-enabled wireless networks are thoroughly investigated. In particular, the key UAV challenges such as three-dimensional (3D) deployment, performance analysis, air-to-ground channel modeling, and energy efficiency are explored along with representative results. Then, fundamental open problems and potential research directions pertaining to wireless communications and networking with UAVs are introduced. To cope with the open research problems, various analytical frameworks and mathematical tools such as optimization theory, machine learning, stochastic geometry, transport theory, and game theory are described. The use of such tools for addressing unique UAV problems is also presented. In a nutshell, this tutorial provides key guidelines on how to analyze, optimize, and design UAV-based wireless communication systems.

In this paper, a planar, compact, single-substrate, multiband 2 sets of 2-elements each multiple-input-multiple-output (MIMO) antenna system is presented. The MIMO antenna system consists of a tunable 2-element meandered and folded MIMO antenna to cover the LTE Band (698 MHz–813 MHz) and a compact 2-element modified truncated cube wideband antenna to cover 754 MHz–971 MHz, 1.65–1.83 GHz and 2–3.66 GHz, respectively. The ground plane of this antenna behaves as a sensing antenna operating in 0.76–1.92 GHz, and 3.0–5.2 GHz. The upper band antennas operate in 0.728–1.08 GHz, 1.64–1.84 GHz, 2.1–3.69 GHz, and 5.01–5.55 GHz range to develop a complete antenna platform for cognitive radios (CR) and Internet of Things (IoT) applications. The antenna is fabricated on a low cost FR-4 substrate (ε<inf>r</inf>=4.4 tanδ=0.02) of dimensions 65 ×120 ×1.56 mm3.

Climate changes and rainfall has been erratic over the past decade. Due to this in recent era, climate-smart methods called as smart agriculture is adopted by many Indian farmers. Smart agriculture is an automated and directed information technology implemented with the IOT (Internet of Things). IOT is developing rapidly and widely applied in all wireless environments. In this paper, sensor technology and wireless networks integration of IOT technology has been studied and reviewed based on the actual situation of agricultural system. A combined approach with internet and wireless communications, Remote Monitoring System (RMS) is proposed. Major objective is to collect real time data of agriculture production environment that provides easy access for agricultural facilities such as alerts through Short Massaging Service (SMS) and advices on weather pattern, crops etc.

A novel pattern-reconfigurable compact planar ultra-wideband monopole antenna is presented. By the incorporation of four p-i-n diode switches and two parasitic elements, the antenna's radiation patterns can be shaped to concentrate energy in specific directions while minimising the gain in other unwanted directions without significantly affecting the impedance bandwidth of the antenna. A fully functional prototype has been developed and tested. The measured results of the return loss, radiation patterns, and realised gain verify the effectiveness of the proposed antenna configuration. The antenna switches its radiation patterns between an omni-directional mode and two directional modes with opposite directions in the operating range from 3 to 6 GHz. The proposed antenna could be a suitable candidate for advanced and smart radio applications such as cognitive radio (CR) as it can enhance the radio front-end flexibility and performance by adding the benefits of pattern diversity, specifically in multipath environments.

In this paper we take an overall look at key technical challenges in the evolution of the Radio Access Network (RAN) architecture towards Cloud RAN, and solutions to overcome them. To address fronthaul limitations, we examine the implications and tradeoffs enabled by functional splits on fronthaul needs, system performance, and centralization scale. We examine the architecture of algorithms for multi-cell coordination and implications in a Cloud RAN environment. To maximize the use of General-Purpose Processors (GPP) and operating systems such as Linux for Cloud RAN, we propose methods of achieving real-time performance suitable for RAN functions. To enable right-sizing the amount of compute used for various RAN functions based on the workload, we propose methods of pooling and elastic scaling for RAN functions that exploit the fact that certain RAN functions perform per-user operations while others perform per-cell operations. Cloud RAN also aims to use cloud management technologies such as virtualized infrastructure management (VIM) and orchestration for automating the instantiation and scaling of RAN functions. We identify special needs for RAN arising from real-time constraints and a mix of GPP and non-GPP hardware. Keywords—Cloud RAN, fronthaul, real-time, pooling, elastic scaling, multi-cell coordination, orchestration, NFV, 5G.


Question: An overview of artificial intelligence applications in cognitive radio systems.

This paper explores the application of simulated annealing as a powerful optimization technique. Simulated annealing is inspired by the annealing process in metallurgy, where a material is heated and then slowly cooled to increase its structural quality. Similarly, in the computational domain, this algorithm starts with an initial solution and iteratively searches for better solutions by allowing occasional moves to worse solutions, guided by a temperature parameter that gradually decreases. This approach enables the algorithm to escape local optima and potentially converge to a global optimum. The versatility of simulated annealing makes it suitable for a wide range of optimization problems, particularly those with complex and multidimensional solution spaces. Its success in finding high-quality solutions has been demonstrated in various fields, including scheduling, network design, and resource allocation. The paper provides a comprehensive review of the simulated annealing algorithm, discussing its theoretical foundations, practical implementation, and application across different domains. It also highlights the parameters that influence the performance of the algorithm, such as the cooling schedule and the acceptance probability, and suggests strategies for tuning these parameters to improve optimization outcomes. Through a series of case studies and comparative analyses, the paper underscores the effectiveness of simulated annealing as a robust optimization tool, capable of addressing challenging problems that are difficult to solve using traditional optimization methods.

**Question**: An Overview of Deep Learning Techniques for Solar Energy Forecasting

**Documents**: A Deep Learning Approach to Solar Energy Forecasting

**Similar Documents**: This article delves into the utilization of deep learning techniques for enhancing the accuracy of solar energy forecasting. Solar energy forecasting is critical for optimizing the integration of renewable energy sources into the power grid and improving the efficiency of energy management systems. The inherent variability and intermittency of solar power generation pose significant challenges to accurate forecasting. To address these issues, deep learning models, with their ability to process complex and high-dimensional data, have shown promising results. This paper discusses various deep learning architectures, such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, that have been successfully applied to solar energy forecasting. CNNs are adept at capturing spatial patterns in meteorological data, while LSTMs excel in handling temporal dependencies. The paper also examines the integration of these models with other data sources, such as satellite imagery and weather forecasts, to improve the predictive capabilities of deep learning systems. Through a detailed analysis of recent studies and applications, the article highlights the advancements in deep learning for solar energy forecasting and identifies potential avenues for future research. It underscores the importance of robust data preprocessing and feature engineering in

Efficiently sharing spectrum among multiple users is critical to wireless network performance. In this paper, we propose a novel spectrum sharing protocol called Collision-Resistant Multiple Access (CRMA) to achieve high efficiency. In CRMA, each transmitter views the OFDM physical layer as multiple orthogonal but sharable channels, and independently selects a few channels for transmission. The transmissions that share the same channel naturally add up in the air. The receiver extracts the received signals from all the channels and efficiently decodes the transmissions by solving a simple linear system. We implement our approach in the Qualnet simulator and show that it yields significant improvement over existing spectrum sharing schemes. We also demonstrate the feasibility of our approach using implementation and experiments on GNU Radios.

telligence are traced to philosophy, fiction, and imagination. Early inventions in electronics, engineering, and many other disciplines have influenced AI. Some early milestones include work in problems solving which included basic work in learning, knowledge representation, and inference as well as demonstration programs in language understanding, translation, theorem proving, associative memory, and knowledge-based systems. The article ends with a brief examination of influential organizations and current issues facing the field.

The idea of Smart Grid has started to evolve more rapidly with the enhancement in Communication Technologies. Two way communication is a key aspect in realizing Smart Grids and is easily possible with the help of modern day advancements in both wired and wireless communication technologies. This paper discusses some of the major communication technologies which include IEEE specified ZigBee, WiMAX and Wireless LAN (Wi-Fi) technologies, GSM 3G/4G Cellular, DASH 7 and PLC (Power Line Communications), with special focus on their applications in Smart Grids. The Smart Grid environments and domains such as Home Area Automation, Substation Automation, Automated Metering Infrastructure, Vehicle-to-Grid Communications, etc. are considered as priority areas for developing smarter grids. The advancements, challenges and the opportunities present in these priority areas are discussed in this paper. & 2012 Elsevier Ltd. All rights reserved.

The aim of the present paper is to review the technical and scientific state of the art of wireless sensor technologies and standards for wireless communications in the Agri-Food sector. These technologies are very promising in several fields such as environmental monitoring, precision agriculture, cold chain control or traceability. The paper focuses on WSN (Wireless Sensor Networks) and RFID (Radio Frequency Identification), presenting the different systems available, recent developments and examples of applications, including ZigBee based WSN and passive, semi-passive and active RFID. Future trends of wireless communications in agriculture and food industry are also discussed.

Trading in financial markets is undergoing a radical transformation, one in which algorithmic methods are becoming increasingly more important. The development of intelligent agents that can act as autonomous traders seems like a logical step forward in this “algorithms arms race”. In this paper we describe an infrastructure for implementing hybrid intelligent agents with the ability to trade financial instruments without requiring human supervision. We used this infrastructure, which relies heavily on artificial intelligence models and problem solving methodologies, to implement two currency trading agents. So far these agents have been able to profit from inefficiencies in the currency market, which lead us to believe our infrastructure can be of practical interest to the investment industry.

Emerging mobile applications involve continuous sensing and complex computations on sensed data streams. Examples include cognitive apps (e.g., speech recognition, natural language translation, as well as face, object, or gesture detection and recognition) and anticipatory apps that proactively track and provide services when needed. Unfortunately, today's mobile devices cannot keep pace with such apps, despite advances in hardware capability. Traditional approaches address this problem by computation offloading. One approach offloads by sending sensed streams to remote cloud servers via cellular networks or to cloudlets via Wi-Fi, where a clone of the app runs [2, 3, 4]. However, cloudlets may not be widely deployed and access to cloud infrastructure may yield high network delays and can be intermittent due to mobility. Morever, users might hesitate to upload private sensing data to the cloud or cloudlet. A second approach offloads to accelerators by rewriting code to use DSP or GPU within mobile devices. However, using accelerators requires substantial programming effort and produces varied benefits for diverse codes on heterogeneous devices.

The ambitious short-term and long-term goals set down by the various national space agencies call for radical advances in several of the main space engineering areas, the design of intelligent space agents certainly being one of them. In recent years, this has led to an increasing interest in artificial intelligence by the entire aerospace community. However, in the current state of the art, several open issues and showstoppers can be identified. In this chapter, we review applications of artificial intelligence in the field of space engineering and space technology and identify open research questions and challenges. In particular, the following topics are identified and discussed: distributed artificial intelligence, enhanced situation self-awareness, and decision support for spacecraft system design.

This overview portrays the evolution of orthogonal frequency division multiplexing (OFDM) research. The amelioration of powerful multicarrier OFDM arrangements with multiple-input multiple-output (MIMO) systems has numerous benefits, which are detailed in this treatise. We continue by highlighting the limitations of conventional detection and channel estimation techniques designed for multiuser MIMO OFDM systems in the so-called rank-deficient scenarios, where the number of users supported or the number of transmit antennas employed exceeds the number of receiver antennas. This is often encountered in practice, unless we limit the number of users granted access in the base station's or radio port's coverage area. Following a historical perspective on the associated design problems and their state-of-the-art solutions, the second half of this treatise details a range of classic multiuser detectors (MUDs) designed for MIMO-OFDM systems and characterizes their achievable performance. A further section aims for identifying novel cutting-edge genetic algorithm (GA)-aided detector solutions, which have found numerous applications in wireless communications in recent years. In an effort to stimulate the cross pollination of ideas across the machine learning, optimization, signal processing, and wireless communications research communities, we will review the broadly applicable principles of various GA-assisted optimization techniques, which were recently proposed also for employment in multiuser MIMO OFDM. In order to stimulate new research, we demonstrate that the family of GA-aided MUDs is capable of achieving a near-optimum performance at the cost of a significantly lower computational complexity than that imposed by their optimum maximum-likelihood (ML) MUD aided counterparts. The paper is concluded by outlining a range of future research options that may find their way into next-generation wireless systems.

Intelligent Transportation Systems (ITS) enable new services within Smart Cities. Efficient Waste Collection is considered a fundamental service for Smart Cities. Internet of Things (IoT) can be applied both in ITS and Smart cities forming an advanced platform for novel applications. Surveillance systems can be used as an assistive technology for high Quality of Service (QoS) in waste collection. Specifically, IoT components: (i) RFIDs, (ii) sensors, (iii) cameras, and (iv) actuators are incorporated into ITS and surveillance systems for efficient waste collection. In this paper we propose an advanced Decision Support System (DSS) for efficient waste collection in Smart Cities. The system incorporates a model for data sharing between truck drivers on real time in order to perform waste collection and dynamic route optimization. The system handles the case of ineffective waste collection in inaccessible areas within the Smart City. Surveillance cameras are incorporated for capturing the problematic areas and provide evidence to the authorities. The waste collection system aims to provide high quality of service to the citizens of a Smart City.

As cities continue to grow, numerous initiatives for Smart Cities are being conducted. The concept of Smart City encompasses several concepts being governance, economy, management, infrastructure, technology and people. This means that a Smart City can have different communication needs. Wireless technologies such as WiFi, ZigBee, Bluetooth, WiMax, 4G or LTE (Long Term Evolution) have presented themselves as solutions to the communication needs of Smart City initiatives. However, as most of them employ unlicensed bands, interference and coexistence problems are increasing. In this paper, the wireless technologies available nowadays for IoT (Internet of Things) in Smart Cities are presented. Our contribution is a review of wireless technologies, their comparison and the problems that difficult coexistence among them. In order to do so, the characteristics and adequacy of wireless technologies to each domain are considered. The problems derived of over-crowded unlicensed spectrum and coexistence difficulties among each technology are discussed as well. Finally, power consumption concerns are addressed.


Question: An overview of artificial intelligence applications in cognitive radio systems.

This paper provides an in-depth exploration of metaheuristics, focusing on their application to combinatorial optimization problems, an area that is witnessing significant growth in both academic and industrial research. Combinatorial optimization problems hold considerable relevance for both scientific and practical domains. Here, we conduct a comprehensive review of the leading metaheuristic approaches from a conceptual standpoint. Our analysis delves into the various components and principles underlying these metaheuristics, aiming to elucidate their commonalities and distinctions. Two pivotal concepts in the metaheuristic domain are intensification and diversification. These concepts play a critical role in shaping the operational dynamics of metaheuristics, acting as both opposing and complementary forces. To better understand the interplay between intensification and diversification, we propose a framework, referred to as the I&D (Intensification and Diversification) framework. This framework facilitates the comparison and integration of different intensification and diversification strategies. By critically evaluating the strengths and weaknesses of various metaheuristic methodologies, we emphasize the significance of hybridizing metaheuristics and integrating them with other optimization techniques. This approach is essential for enhancing the robustness and effectiveness of optimization solutions.

**Question**: A Comparative Study of Artificial Intelligence Techniques for Network Intrusion Detection

**Documents**: A Multi-objective Evolutionary Algorithm for Network Intrusion Detection
Network intrusion detection is a critical component in the defense against cyber attacks. In this paper, we propose a novel multi-objective evolutionary algorithm (MOEA) for network intrusion detection. The proposed MOEA is based on the non-dominated sorting genetic algorithm (NSGA-II) and the strength Pareto evolutionary algorithm (SPEA2). The MOEA is designed to optimize the detection rate and the false alarm rate simultaneously. The performance of the proposed MOEA is evaluated on the KDD Cup 99 data set, which is widely used in the network intrusion detection community. The experimental results show that the proposed MOEA can achieve a better trade-off between the detection rate and the false alarm rate compared to the single-objective evolutionary algorithms. The proposed MOEA is also compared with other multi-objective evolutionary algorithms, such as NSGA-II and SPEA2. The results show that the proposed MOEA can achieve a better balance between the detection rate and the false alarm rate compared to NSGA-II and SPEA2. The proposed MOEA is also compared with other machine learning algorithms, such as support vector machine (SVM) and random forest (RF). The results show that the proposed MO

Vehicular networking has significant potential to enable diverse applications associated with traffic safety, traffic efficiency and infotainment. In this survey and tutorial paper we introduce the basic characteristics of vehicular networks, provide an overview of applications and associated requirements, along with challenges and their proposed solutions. In addition, we provide an overview of the current and past major ITS programs and projects in the USA, Japan and Europe. Moreover, vehicular networking architectures and protocol suites employed in such programs and projects in USA, Japan and Europe are discussed.

Many visions of the future include people immersed in an environment surrounded by sensors and intelligent devices, which use smart infrastructures to improve the quality of life and safety in emergency situations. Ubiquitous communication enables these sensors or intelligent devices to communicate with each other and the user or a decision maker by means of ad hoc wireless networking. Organization and optimization of network resources are essential to provide ubiquitous communication for a longer duration in large-scale networks and are helpful to migrate intelligence from higher and remote levels to lower and local levels. In this paper, distributed energy-efficient deployment algorithms for mobile sensors and intelligent devices that form an Ambient Intelligent network are proposed. These algorithms employ a synergistic combination of cluster structuring and a peer-to-peer deployment scheme. An energy-efficient deployment algorithm based on Voronoi diagrams is also proposed here. Performance of our algorithms is evaluated in terms of coverage, uniformity, and time and distance traveled until the algorithm converges. Our algorithms are shown to exhibit excellent performance.

What machine learning (ML) technique is used for system intelligence implementation in ADAS (advanced driving assistance system)? This paper tries to answer this question. This paper analyzes ADAS and ML independently and then relate which ML technique is applicable to what ADAS component and why. The paper gives a good grasp of the current state-of-the-art. Sample works in supervised, unsupervised, deep and reinforcement learnings are presented; their strengths and rooms for improvements are also discussed. This forms part of the basics in understanding autonomous vehicle. This work is a contribution to the ongoing research in ML aimed at reducing road traffic accidents and fatalities, and the invocation of safe driving.

The concept of using big data (BD) for wireless communication network optimization is no longer new. However, previous work has primarily focused on long-term policies in the network, such as network planning and management. Apart from this, the source of the data collected for analysis/model training is mostly limited to the core network (CN). In this article, we introduce a novel data-driven intelligent radio access network (RAN) architecture that is hierarchical and distributed and operates in real time. We also identify the required data and respective workflows that facilitate intelligent network optimizations. It is our strong belief that the wireless BD (WBD) and machine-learning/artificial-intelligence (AI)-based methodology applies to all layers of the communication system. To demonstrate the superior performance gains of our proposed methodology, two use cases are analyzed with system-level simulations; one is the neural-network-aided optimization for Transmission Control Protocol (TCP), and the other is prediction-based proactive mobility management.

This paper is an overview of technologies that fall under the term augmented reality. Augmented reality refers to a system in which the physical surroundings of a person are mixed with real-time computer generated information creating an enhanced perception of surrounding environment. Being partly virtual and real, augmented reality applications have quite extreme requirements to be practical to use. It also has very much potential in numerous different application areas. These issues make augmented reality both an interesting and challenging subject from scientific and business perspectives.

Efficiently sharing spectrum among multiple users is critical to wireless network performance. In this paper, we propose a novel spectrum sharing protocol called Collision-Resistant Multiple Access (CRMA) to achieve high efficiency. In CRMA, each transmitter views the OFDM physical layer as multiple orthogonal but sharable channels, and independently selects a few channels for transmission. The transmissions that share the same channel naturally add up in the air. The receiver extracts the received signals from all the channels and efficiently decodes the transmissions by solving a simple linear system. We implement our approach in the Qualnet simulator and show that it yields significant improvement over existing spectrum sharing schemes. We also demonstrate the feasibility of our approach using implementation and experiments on GNU Radios.

As a component of the intelligent transportation system (ITS) and one of the concrete applications of mobile ad hoc networks, inter-vehicle communication (IVC) has attracted research attention from both the academia and industry of, notably, US, EU, and Japan. The most important feature of IVC is its ability to extend the horizon of drivers and on-board devices (e.g., radar or sensors) and, thus, to improve road traffic safety and efficiency. This paper surveys IVC with respect to key enabling technologies ranging from physical radio frequency to group communication primitives and security issues. The mobility models used to evaluate the feasibility of these technologies are also briefly described. We focus on the discussion of various MAC protocols that seem to be indispensable components in the network protocol stack of IVC. By analyzing the application requirements and the protocols built upon the MAC layer to meet these requirements, we also advocate our perspective that ad hoc routing protocols and group communication primitives migrated from wired networks might not be an efficient way to support the envisioned applications, and that new coordination algorithms directly based on MAC could be designed for this purpose.

Interconnected everyday objects, either via public or private networks, are gradually becoming reality in modern life — often referred to as the Internet of Things (IoT) or Cyber-Physical Systems (CPS). One stand-out example are those systems based on Unmanned Aerial Vehicles (UAVs). Fleets of such vehicles (drones) are prophesied to assume multiple roles from mundane to high-sensitive applications, such as prompt pizza or shopping deliveries to the home, or to deployment on battlefields for battlefield and combat missions. Drones, which we refer to as UAVs in this paper, can operate either individually (solo missions) or as part of a fleet (group missions), with and without constant connection with a base station. The base station acts as the command centre to manage the drones' activities; however, an independent, localised and effective fleet control is necessary, potentially based on swarm intelligence, for several reasons: 1) an increase in the number of drone fleets; 2) fleet size might reach tens of UAVs; 3) making time-critical decisions by such fleets in the wild; 4) potential communication congestion and latency; and 5) in some cases, working in challenging terrains that hinders or mandates limited communication with a control centre, e.g. operations spanning long period of times or military usage of fleets in enemy territory. This self-aware, mission-focused and independent fleet of drones may utilise swarm intelligence for a), air-traffic or flight control management, b) obstacle avoidance, c) self-preservation (while maintaining the mission criteria), d) autonomous collaboration with other fleets in the wild, and e) assuring the security, privacy and safety of physical (drones itself) and virtual (data, software) assets. In this paper, we investigate the challenges faced by fleet of drones and propose a potential course of action on how to overcome them.

The behavior of a given wireless device may affect the communication capabilities of a neighboring device, notably because the radio communication channel is usually shared in wireless networks. In this tutorial, we carefully explain how situations of this kind can be modelled by making use of game theory. By leveraging on four simple running examples, we introduce the most fundamental concepts of non-cooperative game theory. This approach should help students and scholars to quickly master this fascinating analytical tool without having to read the existing lengthy, economics-oriented books. It should also assist them in modelling problems of their own.

Autonomic communications aim to provide the quality-of-service in networks using self-management mechanisms. It inherits many characteristics from autonomic computing, in particular, when communication systems are running as specialized applications in software-defined networking (SDN) and network function virtualization (NFV)-enabled cloud environments. This paper surveys autonomic computing and communications in the context of software-driven networks, i.e., networks based on SDN/NFV concepts. Autonomic communications create new challenges in terms of security, operations, and business support. We discuss several goals, research challenges, and development issues on self-management mechanisms and architectures in software-driven networks. This paper covers multiple perspectives of autonomic communications in software-driven networks, such as automatic testing, integration, and deployment of network functions. We also focus on self-management and optimization, which make use of machine learning techniques.