Question: Recipient revocable identity-based broadcast encryption: how to exclude certain recipients in IBBE without plaintext knowledge.

This paper introduces a compact group signature scheme designed to achieve both efficiency and robust security. Our proposed scheme generates signatures that are roughly equivalent in size to conventional RSA signatures, assuming a similar level of security. The strength of our group signature scheme is anchored in two cryptographic assumptions: the Strong Diffie-Hellman assumption and a novel concept in bilinear groups known as the Decision Linear assumption. To validate the security of our system, we employ a proof framework set within the random oracle model. This proof draws upon an adapted version of the security criterion for group signatures that was previously outlined by Bellare, Micciancio, and Warinschi, ensuring that our scheme meets stringent security standards while maintaining practicality.

This paper studies the feasibility of privacy-preservingdata mining in epidemiological study. As for the data-miningalgorithm, we focus to a linear multiple regression thatcan be used to identify the most significant factorsamong many possible variables, such as the historyof many diseases. We try to identify the linear model to estimate a lengthof hospital stay from distributed dataset related tothe patient and the disease information. In this paper, we have done experiment usingthe real medical dataset related to stroke andattempt to apply multiple regression with sixpredictors of age, sex, the medical scales, e.g., Japan Coma Scale, and the modified Rankin Scale. Our contributions of this paper include(1) to propose a practical privacy-preserving protocols for linear multiple regressionwith vertically partitioned datasets, and(2) to show the feasibility of the proposed system usingthe real medical dataset distributed into two parties, the hospital who knows the technical details of diseasesduring the patients are in the hospital, and the local government who knows the residence even afterthe patients left hospital. (3) to show the accuracy and the performance of thePPDM system which allows us to estimate the expectedprocessing time with arbitrary number of predictors.

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.

Online social networks (OSNs) are immensely popular, with some claiming over 200 million users. Users share private content, such as personal information or photographs, using OSN applications. Users must trust the OSN service to protect personal information even as the OSN provider benefits from examining and sharing that information. We present Persona, an OSN where users dictate who may access their information. Persona hides user data with attribute-based encryption (ABE), allowing users to apply fine-grained policies over who may view their data. Persona provides an effective means of creating applications in which users, not the OSN, define policy over access to private data. We demonstrate new cryptographic mechanisms that enhance the general applicability of ABE. We show how Persona provides the functionality of existing online social networks with additional privacy benefits. We describe an implementation of Persona that replicates Facebook applications and show that Persona provides acceptable performance when browsing privacy-enhanced web pages, even on mobile devices.

Public blockchains provide a decentralized method for storing transaction data and have many applications in different sectors. In order for a user to track transactions, a simple method is that every user keeps a local copy of the entire public ledger. Since the size of a ledger keeps growing, this method becomes increasingly less practical, especially for lightweight users such as IoT devices and smartphones. In order to deal with this problem, there have been some proposals. However, existing solutions either achieve a limited storage reduction (e.g., simple payment verification), or rely on some strong security assumption (e.g., the use of trusted server). We propose EPBC, a novel and efficient transaction verification scheme for public ledgers, which only requires lightweight users to store a small amount of data that is independent of the size of the blockchain. We analyze EPBC's performance and security, and discuss its integration with existing public ledger systems. Experimental results confirm that EPBC is practical for lightweight users.

—A digital signature is an important public-key primitive that performs the function of conventional handwritten signatures for entity authentication, data integrity, and non-repudiation, especially within the electronic commerce environment. Currently, most conventional digital signature schemes are based on mathematical hard problems. These mathematical algorithms require computers to perform the heavy and complex computations to generate and verify the keys and signatures. In 1995, Naor and Shamir proposed a visual cryptography (VC) for binary images. VC has high security and requires simple computations. The purpose of this paper is to provide an alternative to the current digital signature technology. In this paper, we introduce a new digital signature scheme based on the concept of a non-expansion visual cryptography. A visual digital signature scheme is a method to enable visual verification of the authenticity of an image in an insecure environment without the need to perform any complex computations. Our proposed scheme generates visual shares and manipulates them using the simple Boolean operations OR rather than generating and computing large and long random integer values as in the conventional digital signature schemes currently in use.

An encryption method is presented with the novel property that publicly revealing an encryption key does not thereby reveal the corresponding decryption key. This has two important consequences:Couriers or other secure means are not needed to transmit keys, since a message can be enciphered using an encryption key publicly revealed by the intended recipient. Only he can decipher the message, since only he knows the corresponding decryption key.
A message can be “signed” using a privately held decryption key. Anyone can verify this signature using the corresponding publicly revealed encryption key. Signatures cannot be forged, and a signer cannot later deny the validity of his signature. This has obvious applications in “electronic mail” and “electronic funds transfer” systems. A message is encrypted by representing it as a number M, raising M to a publicly specified power e, and then taking the remainder when the result is divided by the publicly specified product, n, of two large secret prime numbers p and q. Decryption is similar; only a different, secret, power d is used, where e * d = 1(mod (p - 1) * (q - 1)). The security of the system rests in part on the difficulty of factoring the published divisor, n.

This paper describes a cooperative overlay network that provides anonymous communication services for participating users. The Anonymizing Peer-to-Peer Proxy (AP3) system provides clients with three primitives: (i) anonymous message delivery, (ii) anonymous channels, and (iii) secure pseudonyms. AP3 is designed to be lightweight, low-cost and provides "probable innocence" anonymity to participating users, even under a large-scale coordinated attack by a limited fraction of malicious overlay nodes. Additionally, we use AP3's primitives to build novel anonymous group communication facilities (multicast and anycast), which shield the identity of both publishers and subscribers.

Data access control is an effective way to ensure data security in the cloud. However, due to data outsourcing and untrusted cloud servers, the data access control becomes a challenging issue in cloud storage systems. Existing access control schemes are no longer applicable to cloud storage systems, because they either produce multiple encrypted copies of the same data or require a fully trusted cloud server. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising technique for access control of encrypted data. However, due to the inefficiency of decryption and revocation, existing CP-ABE schemes cannot be directly applied to construct a data access control scheme for multiauthority cloud storage systems, where users may hold attributes from multiple authorities. In this paper, we propose data access control for multiauthority cloud storage (DAC-MACS), an effective and secure data access control scheme with efficient decryption and revocation. Specifically, we construct a new multiauthority CP-ABE scheme with efficient decryption, and also design an efficient attribute revocation method that can achieve both forward security and backward security. We further propose an extensive data access control scheme (EDAC-MACS), which is secure under weaker security assumptions.

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 three-party authenticated key exchange protocols, each client shares a secret only with a trusted server with assists in generating a session key used for securely sending messages between two communication clients. Compared with two-party authenticated key exchange protocols where each pair of parties must share a secret with each other, a three-party protocol does not cause any key management problem for the parties. In the literature, mainly there exist three issues in three-party authenticated key exchange protocols are discussed that need to be further improved: (1) to reduce latency, communication steps in the protocol should be as parallel as possible; (2) as the existence of a security-sensitive table on the server side may cause the server to become compromised, the table should be removed; (3) resources required for computation should be as few as possible to avoid the protocol to become an efficiency bottleneck. In various applications over networks, a quick response is required especially by light-weight clients in the mobile e-commerce. In this paper, a roundand computation-efficient three-party authenticated key exchange protocol is proposed which fulfils all of the above mentioned requirements. 2007 Elsevier Inc. All rights reserved.


Question: How to revoke specific recipients in Identity-Based Broadcast Encryption (IBBE) without accessing the plaintext, in the context of Recipient Revocable IBBE?

This paper introduces an innovative identity-based broadcast encryption (IBBE) scheme that achieves constant-size ciphertexts and private keys for the first time. Unlike previous schemes where the size of the public key, ciphertexts, and private keys scaled with the number of possible users in the system, our proposed IBBE scheme optimizes the public key to a size that is linearly dependent on the maximum number of receivers, m, which is typically much smaller than the total number of potential identities. This advancement ensures better scalability and efficiency, particularly in comparison to a recent broadcast encryption system by Boneh, Gentry, and Waters (BGW). While sharing similar properties with the BGW system, our scheme offers a notable improvement in efficiency by reducing the length of the public key. Additionally, the proposed scheme does not impose a fixed limit on the total number of possible users during the system setup, offering greater flexibility for dynamic and large-scale systems.

The large-scale automated transaction systems of the near future can be designed to protect the privacy and maintain the security of both individuals and organizations.

Multicast stream authentication and signing is an important and challenging problem. Applications include the continuous authentication of radio and TV Internet broadcasts, and authenticated data distribution by satellite. The main challenges are fourfold. First, authenticity must be guaranteed even when only the sender of the data is trusted. Second, the scheme needs to scale to potentially millions of receivers. Third, streamed media distribution can have high packet loss. Finally, the system needs to be efficient to support fast packet rates. We propose two efficient schemes, TESLA and EMSS, for secure lossy multicast streams. TESLA, short for Timed Efficient Stream Loss-tolerant Authentication, offers sender authentication, strong loss robustness, high scalability, and minimal overhead, at the cost of loose initial time synchronization and slightly delayed authentication. EMSS, short for Efficient Multi-chained Stream Signature, provides nonrepudiation of origin, high loss resistance, and low overhead, at the cost of slightly delayed verification. This work began in Summer 1999 when Adrian Perrig and Dawn Song were visiting the IBM T. J. Watson research lab. Initial research on stream authentication was done during Summer 1999 by Ran Canetti, Adrian Perrig, and Dawn Song at IBM. Additional improvements were suggested by J. D. Tygar in Fall 1999 at UC Berkeley. Implementation was done in Fall 1999 by Adrian Perrig at UC Berkeley. The work on stream signatures was done by J. D. Tygar, Adrian Perrig, and Dawn Song at UC Berkeley. Additional work was performed by Ran Canetti in Spring 2000. Ran Canetti is at IBM T. J. Watson Research Center, and Adrian Perrig, Dawn Song, and J. D. Tygar are at the Computer Science Division, UC Berkeley. This research was suported in part by the Defense Advanced Research Projects Agency under DARPA contract N6601-99-28913 (under supervision of the Space and Naval Warfare Systems Center San Diego), by the National Science foundation under grant FD99-79852, and by the United States Postal Service under grant USPS 1025 90-98-C-3513. Views and conclusions contained in this document are those of the authors and do not necessarily represent the official opinion or policies, either expressed or implied of the US government or any of its agencies, DARPA, NSF, USPS, or IBM.

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

Multiparty computation (MPC) among n parties can tolerate up to tsynchronous communication setting; however, in an asynchronous communication setting, the resiliency bound decreases to only t < n/3 active corruptions. We improve the resiliency bound for asynchronous MPC (AMPC) to match synchronous MPC using non-equivocation.
 Non-equivocation is a message authentication mechanism to restrict a corrupted sender from making conflicting statements to different (honest) parties. It can be implemented using an increment-only counter and a digital signature oracle, realizable with trusted hardware modules readily available in commodity computers and smartphone devices. A non-equivocation mechanism can also be transferable and allows a receiver to verifiably transfer the authenticated statement to other parties. In this work, using transferable non-equivocation, we present an AMPC protocol tolerating t < n/2 faults. From a practical point of view, our AMPC protocol requires fewer setup assumptions than the previous AMPC protocol with t < n/2 by Beerliova-Trubiniova, Hirt and Nielsen [PODC 2010]: unlike their AMPC protocol, it does not require any synchronous broadcast round at the beginning of the protocol and avoids the threshold homomorphic encryption setup assumption. Moreover, our AMPC protocol is also efficient and provides a gain of Θ(n) in the communication complexity per multiplication gate, over the AMPC protocol of Beerliova-Trubiniova et al. In the process, using non-equivocation, we also define the first asynchronous verifiable secret sharing (AVSS) scheme with t < n/2, which is of independent interest to threshold cryptography.

An encryption method is presented with the novel property that publicly revealing an encryption key does not thereby reveal the corresponding decryption key. This has two important consequences:Couriers or other secure means are not needed to transmit keys, since a message can be enciphered using an encryption key publicly revealed by the intended recipient. Only he can decipher the message, since only he knows the corresponding decryption key.
A message can be “signed” using a privately held decryption key. Anyone can verify this signature using the corresponding publicly revealed encryption key. Signatures cannot be forged, and a signer cannot later deny the validity of his signature. This has obvious applications in “electronic mail” and “electronic funds transfer” systems. A message is encrypted by representing it as a number M, raising M to a publicly specified power e, and then taking the remainder when the result is divided by the publicly specified product, n, of two large secret prime numbers p and q. Decryption is similar; only a different, secret, power d is used, where e * d = 1(mod (p - 1) * (q - 1)). The security of the system rests in part on the difficulty of factoring the published divisor, n.

Freenet is a popular peer to peer anonymous content-sharing network, with the objective to provide the anonymity of both content publishers and retrievers. Despite more than a decade of active development and deployment and the adoption of well-established cryptographic algorithms in Freenet, it remains unanswered how well the anonymity objective of the initial Freenet design has been met. In this paper we develop a traceback attack on Freenet, and show that the originating machine of a content request message in Freenet can be identified; that is, the anonymity of a content retriever can be broken, even if a single request message has been issued by the retriever. We present the design of the traceback attack, and perform both experimental and simulation studies to confirm the feasibility and effectiveness of the attack. For example, with randomly chosen content requesters (and random contents stored in the Freenet testbed), Emulab-based experiments show that, for <inline-formula><tex-math notation="LaTeX">$24$</tex-math> <alternatives><inline-graphic xlink:href="duan-ieq1-2453983.gif"/></alternatives></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$43$</tex-math><alternatives><inline-graphic xlink:href="duan-ieq2-2453983.gif"/> </alternatives></inline-formula> percent of the content request messages, we can identify their originating machines. We also briefly discuss potential solutions to address the developed traceback attack. Despite being developed specifically on Freenet, the basic principles of the traceback attack and solutions have important security implications for similar anonymous content-sharing systems.

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.

Images are inherently sensitive signals that require privacy-preserving solutions when processed in an untrusted environment, but their efficient encrypted processing is particularly challenging due to their structure and size. This work introduces a new cryptographic hard problem called m-RLWE (multivariate Ring Learning with Errors) extending RLWE. It gives support to lattice cryptosystems that allow for encrypted processing of multidimensional signals. We show an example cryptosystem and prove that it outperforms its RLWE counterpart in terms of security against basis-reduction attacks, efficiency and cipher expansion for encrypted image processing.

Deduplication is a widely used technique for reducing storage space of cloud service providers. Yet, it is unclear how to support deduplication of encrypted data securely until the study of Bellareetal on message-locked encryption (Eurocrypt 2013). Since then, there are many improvements such as strengthening its security, reducing client storage, etc. While updating a (shared) file is common, there is little attention on how to efficiently update large encrypted files in a remote storage with deduplication. To modify even a single bit, existing solutions require the trivial and expensive way of downloading and decrypting the large ciphertext.
 We initiate the study of updatable block-level message-locked encryption. We propose a provably secure construction that is efficiently updatable with O(log|F|) computational cost, where |F| is the file size. It also supports proof-of-ownership, a nice feature which protects storage providers from being abused as a free content distribution network.

Many P2P applications require security services such as privacy, anonymity, authentication, and non-repudiation. Such services could be provided through a hierarchical Public Key Infrastructure. However, P2P networks are usually Internet-scale distributed systems comprised of nodes with undetermined trust level, thus making hierarchical solutions unrealistic. In this paper, we propose Chord-PKI, a distributed PKI architecture which is build upon the Chord overlay network, in order to provide security services for P2P applications. Our solution distributes the functionality of a PKI across the peers, by using threshold cryptography and proactive updating. We analyze the security of the proposed infrastructure and through simulations, we evaluate its performance for various scenarios of untrusted node distributions.


Question: How to revoke specific recipients in Identity-Based Broadcast Encryption (IBBE) without accessing the plaintext, focusing on recipient revocable schemes.

This paper presents a novel Identity-Based Encryption (IBE) scheme known as Fuzzy Identity-Based Encryption (FIBE). In contrast to conventional IBE, FIBE treats an identity as a collection of descriptive attributes. A key feature of FIBE is its ability to enable a private key associated with an identity, ω, to decrypt a ciphertext encrypted with a potentially different identity, ω′, but only when these identities exhibit a sufficient level of similarity as determined by a "set overlap" distance metric. This error-tolerant characteristic of FIBE makes it particularly suitable for scenarios where identities are inherently noisy, such as when biometric inputs are used. The inherent robustness against variations in biometric data makes FIBE an ideal choice for encryption systems relying on biometric identities. Moreover, the paper demonstrates that FIBE can facilitate a form of encryption termed "attribute-based encryption," which allows for encryption under a set of attributes that collectively define a (fuzzy) identity. The authors propose two constructions of FIBE schemes, both of which can be seen as IBE of a message under a combination of attributes. These schemes are not only error-tolerant but also secure against collusion attacks. Notably, the fundamental construction avoids the use of random oracles, a feature that contributes to its practicality. The security of these schemes is rigorously analyzed under the Selective-ID security model, ensuring their robustness in real-world applications.

Irrevocability is one major issue in existing bio-cryptosystems. In this paper, we proposed a cancellable bio-cryptosystem by taking the full advantage of cancellable and non-invertible properties of bio-hashing biometrics. Specifically, two transformed templates are generated by using the bio-hashing algorithm and applied into two different secure sketches, fuzzy commitment sketch and fuzzy vault sketch, respectively. These two secure sketches can be fused in two different ways: AND fusion and OR fusion, so as to emphasis either on the recognition accuracy or the security level of the system. Experimental results and security analysis show the validity of the proposed scheme.

Many P2P applications require security services such as privacy, anonymity, authentication, and non-repudiation. Such services could be provided through a hierarchical Public Key Infrastructure. However, P2P networks are usually Internet-scale distributed systems comprised of nodes with undetermined trust level, thus making hierarchical solutions unrealistic. In this paper, we propose Chord-PKI, a distributed PKI architecture which is build upon the Chord overlay network, in order to provide security services for P2P applications. Our solution distributes the functionality of a PKI across the peers, by using threshold cryptography and proactive updating. We analyze the security of the proposed infrastructure and through simulations, we evaluate its performance for various scenarios of untrusted node distributions.

In this demo proposal, we illustrate ACStream, a system built on top of Stream Base [1], to specify and enforce access control policies over data streams. ACStream supports a very flexible role-based access control model specifically designed to protect against unauthorized access to streaming data. The core component of ACStream is a query rewriting mechanism that, by exploiting a set of secure operators proposed by us in [2], rewrites a user query in such a way that it does not violate the specified access control policies during its execution. The demo will show how policies modelling a variety of access control requirements can be easily specified and enforced using ACStream.

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.

Resource and information protection plays a relevant role in distributed systems like the ones present in the Internet of Things (IoT). Authorization frameworks like RBAC and ABAC do not provide scalable, manageable, effective, and efficient mechanisms to support distributed systems with many interacting services and are not able to effectively support the dynamicity and scaling needs of IoT contexts that envisage a potentially unbound number of sensors, actuators and related resources, services and subjects, as well as a more relevance of short-lived, unplanned and dynamic interaction patterns. Furthermore, as more end-users start using smart devices (e.g. smart phones, smart home appliances, etc.) the need to have more scalable, manageable, understandable and easy to use access control mechanisms increases. This paper describes a capability based access control system that enterprises, or even individuals, can use to manage their own access control processes to services and information. The proposed mechanism supports rights delegation and a more sophisticated access control customization. The proposed approach is being developed within the European FP7 IoT@Work project to manage access control to some of the project’s services deployed in the shop floor.

In this paper, a novel visual secret sharing (VSS) scheme based on QR code (VSSQR) with (k, n) threshold is investigated. Our VSSQR exploits the error correction mechanism in the QR code structure, to generate the bits corresponding to shares (shadow images) by VSS from a secret bit in the processing of encoding QR. Each output share is a valid QR code that can be scanned and decoded utilizing a QR code reader, which may reduce the likelihood of attracting the attention of potential attackers. Due to different application scenarios, two different recovered ways of the secret image are given. The proposed VSS scheme based on QR code can visually reveal secret image with the abilities of stacking and XOR decryptions as well as scan every shadow image, i.e., a QR code, by a QR code reader. The secret image could be revealed by human visual system without any computation based on stacking when no lightweight computation device. On the other hand, if the lightweight computation device is available, the secret image can be revealed with better visual quality based on XOR operation and could be lossless revealed when sufficient shares are collected. In addition, it can assist alignment for VSS recovery. The experiment results show the effectiveness of our scheme.

As organizations increase their reliance on, possibly distributed, information systems for daily business, they become more vulnerable to security breaches even as they gain productivity and efficiency advantages. Though a number of techniques, such as encryption and electronic signatures, are currently available to protect data when transmitted across sites, a truly comprehensive approach for data protection must also include mechanisms for enforcing access control policies based on data contents, subject qualifications and characteristics, and other relevant contextual information, such as time. It is well understood today that the semantics of data must be taken into account in order to specify effective access control policies. Also, techniques for data integrity and availability specifically tailored to database systems must be adopted. In this respect, over the years, the database security community has developed a number of different techniques and approaches to assure data confidentiality, integrity, and availability. However, despite such advances, the database security area faces several new challenges. Factors such as the evolution of security concerns, the "disintermediation" of access to data, new computing paradigms and applications, such as grid-based computing and on-demand business, have introduced both new security requirements and new contexts in which to apply and possibly extend current approaches. In this paper, we first survey the most relevant concepts underlying the notion of database security and summarize the most well-known techniques. We focus on access control systems, on which a large body of research has been devoted, and describe the key access control models, namely, the discretionary and mandatory access control models, and the role-based access control (RBAC) model. We also discuss security for advanced data management systems, and cover topics such as access control for XML. We then discuss current challenges for database security and some preliminary approaches that address some of these challenges.

We present a novel approach to fully homomorphic encryption (FHE) that dramatically improves performance and bases security on weaker assumptions. A central conceptual contribution in our work is a new way of constructing leveled, fully homomorphic encryption schemes (capable of evaluating arbitrary polynomial-size circuits of a-priori bounded depth), without Gentry’s bootstrapping procedure.
 Specifically, we offer a choice of FHE schemes based on the learning with error (LWE) or Ring LWE (RLWE) problems that have 2 λ security against known attacks. We construct the following.
 (1) A leveled FHE scheme that can evaluate depth-L arithmetic circuits (composed of fan-in 2 gates) using O(λ. L3) per-gate computation, quasilinear in the security parameter. Security is based on RLWE for an approximation factor exponential in L. This construction does not use the bootstrapping procedure.
 (2) A leveled FHE scheme that can evaluate depth-L arithmetic circuits (composed of fan-in 2 gates) using O(λ2) per-gate computation, which is independent of L. Security is based on RLWE for quasipolynomial factors. This construction uses bootstrapping as an optimization.
 We obtain similar results for LWE, but with worse performance. All previous (leveled) FHE schemes required a per-gate computation of Ω(λ3.5), and all of them relied on subexponential hardness assumptions.
 We introduce a number of further optimizations to our scheme based on the Ring LWE assumption. As an example, for circuits of large width (e.g., where a constant fraction of levels have width Ω(λ)), we can reduce the per-gate computation of the bootstrapped version to O(λ), independent of L, by batching the bootstrapping operation. At the core of our construction is a new approach for managing the noise in lattice-based ciphertexts, significantly extending the techniques of Brakerski and Vaikuntanathan [2011b].

By using Cloud storage, users can access applications, services, software whenever they requires over the internet. Users can put their data remotely to cloud storage and get benefit of on-demand services and application from the resources. The cloud must have to ensure data integrity and security of data of user. The issue about cloud storage is integrity and privacy of data of user can arise. To maintain to overkill this issue here, we are giving public auditing process for cloud storage that users can make use of a third-party auditor (TPA) to check the integrity of data. Not only verification of data integrity, the proposed system also supports data dynamics. The work that has been done in this line lacks data dynamics and true public auditability. The auditing task monitors data modifications, insertions and deletions. The proposed system is capable of supporting public auditability, data dynamics and Multiple TPA are used for the auditing process. We also extend our concept to ring signatures in which HARS scheme is used. Merkle Hash Tree is used to improve block level authentication. Further we extend our result to enable the TPA to perform audits for multiple users simultaneously through Batch auditing.


Question: How to revoke specific recipients in Recipient Revocable Identity-Based Broadcast Encryption (IBBE) without accessing the plaintext?

In this paper, we introduce a set of efficient and secure techniques for conducting searches on data that has been encrypted, addressing the challenge of balancing functionality with security on data storage servers, including mail and file servers. Traditionally, storing data in encrypted form on these servers has often led to a compromise in functionality, particularly in scenarios where a user desires to retrieve specific documents based on the presence of certain keywords. Our cryptographic schemes offer a solution to this problem, enabling data storage servers to perform searches and respond to queries without compromising the confidentiality of the data.

The proposed techniques offer several critical advantages. Firstly, they are provably secure, ensuring that the untrusted server cannot glean any information about the plaintext data simply by having access to the ciphertext. Secondly, they provide query isolation, meaning the untrusted server cannot infer any additional information about the plaintext beyond the search results. Additionally, the schemes support controlled searching, preventing the server from conducting arbitrary searches without the user's explicit authorization. Furthermore, they enable hidden queries, allowing users to request searches for secret words without disclosing the actual word to the server.

The algorithms we have developed are not only secure but also highly efficient. For a document of length n, our encryption and search algorithms require only O(n) stream cipher and block cipher operations, making them simple, fast, and practical for real-world use with minimal space and communication overhead.

We would like to acknowledge the substantial support for this research from various US government agencies. This research has been partially funded by the Defense Advanced Research Projects Agency (DARPA) under contract N6601-9928913, supervised by the Space and Naval Warfare Systems Center San Diego, by the National Science Foundation (NSF) under grant FD99-79852, and by the United States Postal Service (USPS) under grant USPS 102590-98-C3513. The views and conclusions presented in this document are solely those of the authors and do not necessarily reflect the official position or policies of the US government or any of its agencies, including DARPA, NSF, and USPS.

A new signature scheme is proposed, together with an implementation of the Diffie-Hellman key distribution scheme that achieves a public key cryptosystem. The security of both systems relies on the difficulty of computing discrete logarithms over finite fields.

Secure instant messaging is utilized in two variants: one-to-one communication and group communication. While the first variant has received much attention lately (Frosch et al., EuroS Cohn-Gordon et al., EuroS Kobeissi et al., EuroS&P17), little is known about the cryptographic mechanisms and security guarantees of secure group communication in instant messaging. To approach an investigation of group instant messaging protocols, we first provide a comprehensive and realistic security model. This model combines security and reliability goals from various related literature to capture relevant properties for communication in dynamic groups. Thereby the definitions consider their satisfiability with respect to the instant delivery of messages. To show its applicability, we analyze three widely used real-world protocols: Signal, WhatsApp, and Threema. By applying our model, we reveal several shortcomings with respect to the security definition. Therefore we propose generic countermeasures to enhance the protocols regarding the required security and reliability goals. Our systematic analysis reveals that (1) the communications' integrity – represented by the integrity of all exchanged messages – and (2) the groups' closeness – represented by the members' ability of managing the group – are not end-to-end protected. We additionally show that strong security properties, such as Future Secrecy which is a core part of the one-to-one communication in the Signal protocol, do not hold for its group communication.

Multiparty computation (MPC) among n parties can tolerate up to tsynchronous communication setting; however, in an asynchronous communication setting, the resiliency bound decreases to only t < n/3 active corruptions. We improve the resiliency bound for asynchronous MPC (AMPC) to match synchronous MPC using non-equivocation.
 Non-equivocation is a message authentication mechanism to restrict a corrupted sender from making conflicting statements to different (honest) parties. It can be implemented using an increment-only counter and a digital signature oracle, realizable with trusted hardware modules readily available in commodity computers and smartphone devices. A non-equivocation mechanism can also be transferable and allows a receiver to verifiably transfer the authenticated statement to other parties. In this work, using transferable non-equivocation, we present an AMPC protocol tolerating t < n/2 faults. From a practical point of view, our AMPC protocol requires fewer setup assumptions than the previous AMPC protocol with t < n/2 by Beerliova-Trubiniova, Hirt and Nielsen [PODC 2010]: unlike their AMPC protocol, it does not require any synchronous broadcast round at the beginning of the protocol and avoids the threshold homomorphic encryption setup assumption. Moreover, our AMPC protocol is also efficient and provides a gain of Θ(n) in the communication complexity per multiplication gate, over the AMPC protocol of Beerliova-Trubiniova et al. In the process, using non-equivocation, we also define the first asynchronous verifiable secret sharing (AVSS) scheme with t < n/2, which is of independent interest to threshold cryptography.

We often send our confidential information such as passport, credit card, social security numbers over email without concern about the security of email services. Existing network security mechanisms provide adequate security from external malicious adversaries and eavesdroppers, but they don't guarantee that the email service providers (ESPs) wouldn't or can't access our email data themselves, which in some cases could be highly confidential. One of the ways to protect email data from ESPs is to use Pretty Good Privacy (PGP) that has many limitations including key storage problem and dependability on third party services, making it cumbersome to use in practice. In this paper, we present S3Email method that provides email security against ESPs. The proposed method uses a cryptographic secret sharing technique in a novel way and encrypts the email metadata, body and attachments before the email is sent. In the proposed solution, the email sender and receiver must have at least two email accounts on the existing ESPs, which is not unusual today. Experiments and analysis show that the S3Email method provides information theoretic security with minimal computational overhead.

We describe an RSA-based signing scheme which combines essentially optimal e ciency with attractive security properties. Signing takes one RSA decryption plus some hashing, veri cation takes one RSA encryption plus some hashing, and the size of the signature is the size of the modulus. Assuming the underlying hash functions are ideal, our schemes are not only provably secure, but are so in a tight way| an ability to forge signatures with a certain amount of computational resources implies the ability to invert RSA (on the same size modulus) with about the same computational e ort. Furthermore, we provide a second scheme which maintains all of the above features and in addition provides message recovery. These ideas extend to provide schemes for Rabin signatures with analogous properties; in particular their security can be tightly related to the hardness of factoring. Department of Computer Science and Engineering, Mail Code 0114, University of California at San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA. E-mail: mihir@cs.ucsd.edu ; Web page: http://www-cse.ucsd.edu/users/mihir y Department of Computer Science, University of California at Davis, Davis, CA 95616, USA. Email: rogaway@cs.ucdavis.edu ; Web page: http://wwwcsif.cs.ucdavis.edu/~rogaway/homepage.html

In this paper, we introduce a new biometric verification and template protection system which we call THRIVE. The system includes novel enrollment and authentication protocols based on threshold homomorphic encryption where a private key is shared between a user and a verifier. In the THRIVE system, only encrypted binary biometric templates are stored in a database and verification is performed via homomorphically randomized templates, thus, original templates are never revealed during authentication. Due to the underlying threshold homomorphic encryption scheme, a malicious database owner cannot perform full decryption on encrypted templates of the users in the database. In addition, security of the THRIVE system is enhanced using a two-factor authentication scheme involving user’s private key and biometric data. Using simulation-based techniques, the proposed system is proven secure in the malicious model. The proposed system is suitable for applications where the user does not want to reveal her biometrics to the verifier in plain form, but needs to prove her identity by using biometrics. The system can be used with any biometric modality where a feature extraction method yields a fixed size binary template and a query template is verified when its Hamming distance to the database template is less than a threshold. The overall connection time for the proposed THRIVE system is estimated to be 336 ms on average for 256-bit biometric templates on a desktop PC running with quad core 3.2 GHz CPUs at 10 Mbit/s up/down link connection speed. Consequently, the proposed system can be efficiently used in real-life applications.

In this paper we describe a practical digital identity project of a global scale, which solves a number of privacy and scalability problems using the concepts of anonymous credentials and permissioned blockchains. Even though both ideas have been known and developed for a number of years, our system appears to be the first amalgamation. The prototype has been developed, tested, and published as open source. I. DIGITAL IDENTITY: HISTORY AND CHALLENGES The idea of digital identity can be traced to the beginning of the computer era, when the user profiles were first transferred across networks. In the real world, different authorities issue licenses, passports, identification cards, credit cards, checks, etc., generally called credentials, to ordinary users for their presentation to various service or goods providers. This brings the concept of Issuer, User, and Verifier as subjects of the identity system. It immediately became clear that issuance, storage, and presentation procedures must follow a number of security requirements to be competitive with the traditional paper credentials: • Compatibility. Credentials issued by different Issuers can be combined for a single presentation by User who is required to prove several properties simultaneously: e.g. that he is at least age 18 employed by XYZ Corp. with a driver license and good eyesight (does not require corrective lenses). • Unforgeability. A malicious User is unable to present a credential not issued by a legitimate Issuer. • Scalability. A system must handle hundreds of Issuers and billions of Users, such as an international passport system. It might be required to handle thousands of transactions per second like the Visa system [1]. • Performance/Low latency. Verification of credentials must be almost instant and not require broadband network connectivity per credential, even if it is issued by a previously unknown Issuer. Credentials can be stored and processed easily by the User even if he is offline. • Revocation. An Issuer can revoke any credential such that a User and any Verifiers can know within a reasonable amount of time that credential was revoked. Additionally, a digital identity system could provide features impossible or difficult to implement in a paper identity system: • Minimal dependencies. Aside from an earlier issuance, an Issuer should not be involved during the preparation and presentation of proofs, and the verification of credentials, including proof of nonrevocation. • Privacy/Anonymity. The real identity of a User can be kept secret from Verifiers who do not need it. • Unlinkability. Different presentations of the same credential cannot be linked together. • Selective disclosure. Any subset of attributes embedded in a credential can be kept secret from Verifiers. The latter group of properties is particularly attractive for Users from authoritarian countries or those who are concerned about leaks of their data from careless Issuers or Verifiers. The privacy-oriented digital identity schemes have been proposed as U-Prove [2] (mainly Microsoft, primarily based on Brands’ credentials [3]) and Idemix [4] (mainly IBM, primarily based on the Camenisch-Lysyanskaya credentials [5]), with the ABC4Trust and FIDO [6] projects encompassing these two. Despite significant effort expended on both projects, neither is in widespread use today. We deduce that all these systems lack compatibility and scalability, as an actual implementation would require a meta-system that congregates multiple Issuers and Verifiers and manages credential schemas. Before recently, all such system would require a (trusted) global third party for the exchange and distribution of Issuers’ data and parameters. It seems that such a centralized party on a global scale could not exist. II. OUR CONTRIBUTIONS We designed and implemented a system called Sovrin, which integrates anonymous credentials with revocation [7], [8] for privacy, unforgeability, performance, unlinkability and a distributed ledger, taking best practices from Ethereum [9] and BFT protocols. The implementation is mostly finished and will go live in the near future. A. Anonymous credentials for privacy It is well known that the anonymity and unlinkability properties are provided by various kinds of anonymous credentials, the concept dating back to seminal works by David Chaum in 1980s [10] and refined in a number of follow-up papers including well-known [3], [7], [11]. Zeroknowledge proofs allow a User to prove the possession of a credential without showing the credential itself, thus providing unlinkability. Additional features include delegation [11] and revocation [8]. We reference the Idemix specification [4], [12] as the basis for our anonymous credential module as it provides unlinkability by default (in contrast to U-Prove). Although Idemix is available as a Java library, for portability and fast prototyping we select the Charm framework [13], which provides a Python API for large integers, signature schemes, and pairings. It also allows us to integrate the pairing-based revocation module easily. B. Our treatment of the revocation feature The revocation feature was missing in the original papers behind U-Prove and Idemix and was added later in different ways. The revocation procedure assumes that each credential has special Revocation-ID attribute with value iR, and Issuer can at any moment revoke a particular iR. For Verifiers this means that if a User with a revoked iR presents his credential, this can be detected by the Verifier. We observe that in addition to various drawbacks described above, the revocation feature provides quite a privacy leak. Indeed, consider a typical attack setting: a Verifier colludes with an Issuer to disclose the identity of a User. The Issuer then revokes half of the issued credentials, and the Verifier asks the User to update his revocation data. If the User succeeds in presenting a valid non-revocation proof, he is among the users with non-revoked iR. Then the Issuer reinstates the earlier revocations and revokes another group of revocation IDs of equal size. Repeating this procedure logN times, the Issuer-Verifier team can uniquely identify one of N users. C. Our selection of revocation methods Since 2001, the following revocation methods have been proposed: • Signature lists [14]. Issuer regularly releases a list of signatures, one per non-revoked iR. User proves that his credential has the same ID as in some signature. Verification is fast, but the amount of data retrieved by User and computation he does is huge, and multiple presentations are linkable. • Cryptographic accumulators [7], [8]. The issued iR is added to (or removed from) a constant-size accumulator A, and a User must prove that his (non-disclosed) ID is still contained in A. This approach is reasonably fast for all parties. • Forward revocation lists [15]. For each revoked iR, an Issuer generates a revocation entry f(iR). A User presents a non-revocation proof to a Verifier, who then tests the proof against every entry in the list. If all matches fail, the credential is deemed valid. This method requires significant computational resources from the Verifier. We decided to use accumulators based on bilinear maps [8] for performance reasons. The small order of the target group allows for short accumulators and witnesses. We have our own implementation of accumulators, which is based on a corrected version of paper [8]. The drawback is that the User must be aware of all revoked credentials, as the proof is updated every time another credential is revoked and needs (public) Issuer-specific data for the update. If User has small capacity, the data he needs to update the nonrevocation proof reveals his ID. Existing research papers suggest User connecting to Issuer every time before creating a non-revocation proof [8], which is privacy-vulnerable if Issuer or his agent colludes with Verifier. D. Revocation with attribute-based sharding. The accumulator-based revocation procedure requires the User to maintain his non-revocation data w using the indices {iR} of the credentials revoked and issued from the last update. To update w the User also needs to access a global set G = {gi} of validity tails, published by Issuer. The tail set G is immutable but should be generated in advance and occupy quite big space (256 bytes per credential). Even though the number of tails needed for the update is proportional to the number of revoked credentials in the period, the tails retrieved by the User effectively disclose his own index iR and thus his identity. For a global Issuer the User would be unable to carry the entire set of tails at his device, as they would require GBytes of storage. To prevent the privacy leak, we propose to partition the credential IDs I into shards I1, I2, . . . , Is of limited size so that the tail set G(Ij) for each shard would be feasible to download and carry (say, a dozen of MBytes). Together with the credential to present, the User will then inform the Verifier of his shard number j so that the Verifier uses the corresponding accumulator data Aj . To make the shard index privacy-oblivious, we suggest attribute-based sharding, when Issuer selects q attributes {ai} of necessary granularity (year of birth, zip code, etc.) and partitions the credential set by each ai independently. For each attribute ai and each shard an accumulator Ai,j will be published. Nevertheless, since accumulators are of constant size, the total amount of space consumed by q× s accumulators is negligible. At the time of presentation the User selects the attribute on which he will prove the nonrevocation. This attribute should be among those that are already disclosed to the Verifier. Before presentation, User downloads the entire set Gi,j of tails that correspond to his share j at attribute i. As the tails are signed, he ask the Verifier to download, and this does not reduce the privacy. We also implement the revocati

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 2008, Beck and Tews have proposed a practical attack on WPA. Their attack (called the Beck-Tews attack) can recover plaintext from an encrypted short packet, and can falsify it. The execution time of the Beck-Tews attack is about 12-15 minutes. However, the attack has the limitation, namely, the targets are only WPA implementations those support IEEE802.11e QoS features. In this paper, we propose a practical message falsification attack on any WPA implementation. In order to ease targets of limitation of wireless LAN products, we apply the Beck-Tews attack to the man-in-the-middle attack. In the man-inthe-middle attack, the user’s communication is intercepted by an attacker until the attack ends. It means that the users may detect our attack when the execution time of the attack is large. Therefore, we give methods for reducing the execution time of the attack. As a result, the execution time of our attack becomes about one minute in the best case.

Sharing of resources on the cloud can be achieved on a large scale, since it is cost effective and location independent. Despite the hype surrounding cloud computing, organizations are still reluctant to deploy their businesses in the cloud computing environment due to concerns in secure resource sharing. In this paper, we propose a cloud resource mediation service offered by cloud service providers, which plays the role of trusted third party among its different tenants. This paper formally specifies the resource sharing mechanism between two different tenants in the presence of our proposed cloud resource mediation service. The correctness of permission activation and delegation mechanism among different tenants using four distinct algorithms (activation, delegation, forward revocation, and backward revocation) is also demonstrated using formal verification. The performance analysis suggests that the sharing of resources can be performed securely and efficiently across different tenants of the cloud.


Question: An experiment on the willingness to sell and the willingness to protect personal information when 25 cents is considered too high.

In the realm of personal data protection, the intricate relationship between privacy, rationality, and consumer decision-making is a topic of significant interest. Conventionally, economic theory posits that individuals should possess the capability to effectively manage their own privacy concerns. However, both empirical studies and theoretical frameworks have highlighted that consumers frequently find themselves in a disadvantaged position, lacking the comprehensive information necessary to make informed choices that safeguard their privacy. Even when armed with adequate knowledge, there is a tendency for individuals to prioritize immediate gains over the long-term implications of privacy erosion, revealing a complex interplay between short-term gratification and the enduring value of personal information security.

**Question**: The Role of Public-Private Partnerships in Renewable Energy Projects: A Case Study of Solar Power in Rural India

**Documents**: Public-Private Partnerships for Sustainable Energy: Lessons from Indonesia
The paper discusses the role of public-private partnerships (PPPs) in the implementation of sustainable energy projects in Indonesia. PPPs can bring together the financial resources and expertise of both the public and private sectors to achieve common goals, such as increasing access to clean energy in remote areas.

**Similar Documents**: Exploring the Dynamics of Public-Private Collaborations in Renewable Energy: Insights from Rural Solar Initiatives in India
This document delves into the significance of public-private partnerships (PPPs) in driving renewable energy initiatives, particularly focusing on solar power projects in rural India. By examining case studies and drawing lessons from successful collaborations, it highlights how PPPs can effectively mobilize the combined strengths of the public and private sectors. These partnerships facilitate the pooling of financial resources, technical expertise, and operational capabilities, which are crucial for expanding access to clean energy in underserved regions. Through strategic alliances, both sectors can work towards shared objectives, ensuring the sustainability and scalability of renewable energy solutions in rural areas.


Question: An experiment on the willingness to sell and protect personal information when 25 cents is deemed excessive.

Navigating the Balance: An Empirical Study of Personalization and Privacy in Online Consumer Behavior
The concept of personalization, involving the customization of products and purchasing experiences according to individual consumer preferences, has gained significant traction with the advent of advanced information acquisition and processing technologies. Online retailers now have the capability to offer a range of personalized services that not only enhance customer loyalty by increasing switching costs but also serve as a crucial tool for collecting valuable customer data. Yet, this strategy's effectiveness is contingent upon consumers' willingness to engage with personalized services amidst growing privacy concerns. Given the lack of empirical research addressing the consumer's dilemma between personalization benefits and privacy risks, our study develops a concise model to forecast consumer usage of online personalization services. This model takes into account the tradeoff between the perceived value of personalization and privacy concerns, and highlights the role of trust in the vendor as a key factor in consumers' decision-making process. Our results indicate that: 1. online retailers can optimize their data collection and utilization efforts by fostering trust with their customers; 2. it is imperative for retailers to recognize and assess the diverse values consumers associate with various forms of personalization, as this understanding is essential for the success of personalization strategies.


Question: An experiment on the willingness to sell and the willingness to protect personal information when 25 cents is considered too high.

This document explores the intricate dynamics of conditioning prices based on consumers' purchase history in an era where technological advancements enable sellers to tailor price offers according to prior buying patterns. The investigation delves into the circumstances under which such a form of price discrimination becomes economically advantageous, particularly when consumers possess the agency and strategies to safeguard their privacy. The foundational model posits rational consumers with stable preferences for the merchandise and a monopolistic retailer capable of locking into a pricing strategy. Drawing from established literature, it is revealed that while it is technically possible to adjust prices to differentiate between high and low valuation customers, the merchant will, surprisingly, never find this strategy to be optimally profitable. The analysis then broadens, considering several modifications to this baseline scenario. These include scenarios where the seller provides premium services to repeat buyers, situations where the retailer cannot adhere rigidly to a pricing plan, and environments characterized by competitive market forces. In these more complex settings, it is demonstrated that sellers typically stand to gain by conditioning their prices on a customer's purchase history, highlighting the nuanced interplay between privacy, consumer behavior, and pricing strategies.


Question: An experiment on the willingness to sell and the willingness to protect personal information when the price is only 25 cents.

This paper explores the complexities of privacy decisions in electronic commerce, highlighting inconsistencies between stated privacy concerns and actual behavior. By drawing on insights from behavioral economics, we delve into the individual decision-making process regarding privacy in online transactions. We argue that expecting fully rational behavior in this domain is unrealistic. Instead, models that consider self-control issues and the allure of immediate gratification provide a more accurate depiction of how decisions are made, aligning more closely with empirical evidence. Specifically, we illustrate how individuals, even those with genuine privacy concerns, may fail to protect their information due to psychological biases extensively documented in behavioral research. These biases can influence not only inexperienced users but also those considered more knowledgeable about privacy risks. Furthermore, we demonstrate that these biases can persist even when individuals are acutely aware of the potential dangers associated with failing to safeguard their personal data.


Question: An experiment on the willingness to sell and protect personal information when 25 cents is considered excessive.

This study delves into the intricacies of privacy concerns and preferences among users in the realm of electronic commerce. Recognizing that transactions often necessitate the disclosure of personal information such as shipping addresses, payment details, or purchasing habits, the research explores how users perceive and react to potential privacy invasions. The reluctance to share such data, or even to engage in online browsing, when privacy is perceived to be at risk, underscores the critical nature of privacy in e-commerce.

One technological solution aimed at addressing these privacy concerns is P3P (Platform for Privacy Preferences Project), developed by the World Wide Web Consortium. P3P is designed to empower users to manage their privacy preferences and control the information they share online. However, to effectively design user-friendly and impactful privacy interfaces, a deeper understanding of the spectrum of user concerns and preferences is required.

To this end, the study surveyed 381 U.S. Internet users, presenting them with various e-commerce scenarios and gauging their privacy concerns and preferences. The results of this survey not only shed light on the nuanced attitudes towards privacy among online consumers but also offer valuable insights for the design and implementation of privacy technologies like P3P. The paper discusses the key findings of the study and their implications for interface design, highlighting the need for privacy solutions that are both effective and user-centric.


Question: Identifying fruits and vegetables through the application of machine learning techniques.

This paper investigates the efficacy of heuristic evaluations in assessing user interface designs. Heuristic evaluation is a less formal method involving multiple evaluators who review an interface and provide feedback. However, the study revealed that individual evaluators typically performed poorly in identifying usability issues, detecting only 20 to 51% of the problems in the interfaces they assessed. Interestingly, the results improved significantly when aggregating the evaluations from multiple evaluators. Aggregated evaluations, even with just three to five evaluators, were found to be highly effective and accurate in pinpointing usability flaws. This suggests that collaborative heuristic evaluations can serve as a valuable tool for identifying and addressing user interface design issues.

**Question**: The Impact of Social Media on Mental Health

**Documents**: The effects of social media on the mental health of adolescents
The proliferation of social media has led to significant changes in how adolescents interact and communicate. This study aims to examine the effects of social media on the mental health of adolescents. A survey was conducted on a sample of 500 adolescents aged between 13 and 18 years. The results show that there is a significant correlation between the amount of time spent on social media and the level of anxiety and depression reported by adolescents. Specifically, adolescents who spend more than three hours per day on social media are more likely to experience symptoms of anxiety and depression than those who spend less time on these platforms. Additionally, the study found that the quality of social interactions on social media is a key factor in determining the impact on mental health. Adolescents who reported having positive social interactions on social media had lower levels of anxiety and depression compared to those who reported negative interactions. Overall, the study suggests that social media can have both positive and negative effects on the mental health of adolescents, depending on the amount of time spent on these platforms and the quality of social interactions.

**Similar Documents**: This research explores the relationship between social media usage and its effects on adolescent mental health. With the widespread adoption of social media, the dynamics of adolescent communication and socialization have transformed. To understand the implications of this shift, a survey was conducted on a group of 500 adolescents aged 13 to 18. The findings reveal a clear association between the duration of social media engagement and the incidence of anxiety and depression among adolescents. Those who dedicate over three hours daily to social media platforms are more prone to anxiety and depressive symptoms than their peers with less screen time. Moreover, the nature of online social exchanges plays a critical role in mental health outcomes. Adolescents

Computer-aided diagnosis systems have been the focus of many research endeavors. They are based on the idea of processing and analyzing images of different parts of the human body for a quick and accurate diagnosis. In this paper, the aforementioned approach is followed to detect whether a brain hemorrhage exists or not in a Computed Topography (CT) scans of the brain. Moreover, the type of the hemorrhage is identified. The implemented system consists of several stages that include image preprocessing, image segmentation, feature extraction, and classification. The results of the conducted experiments are very promising. A recognition rate of 100% is attained for detecting whether a brain hemorrhage exists or not. For the hemorrhage type classification, more than 92% accuracy is achieved. Key–Words:brain hemorrhage, brain ct scans, machine learning, image processing, image segmentation

Unmanned aerial vehicles (UAVs) and other robots in smart farming applications offer the potential to monitor farm land on a per-plant basis, which in turn can reduce the amount of herbicides and pesticides that must be applied. A central information for the farmer as well as for autonomous agriculture robots is the knowledge about the type and distribution of the weeds in the field. In this regard, UAVs offer excellent survey capabilities at low cost. In this paper, we address the problem of detecting value crops such as sugar beets as well as typical weeds using a camera installed on a light-weight UAV. We propose a system that performs vegetation detection, plant-tailored feature extraction, and classification to obtain an estimate of the distribution of crops and weeds in the field. We implemented and evaluated our system using UAVs on two farms, one in Germany and one in Switzerland and demonstrate that our approach allows for analyzing the field and classifying individual plants.

Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on graytone spatial dependancies, and illustrates their application in categoryidentification tasks of three different kinds of image data: photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Technology Satellite (ERTS) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.

This paper presents a hierarchical classification system based on the kNearest Neighbors (kNN) classifier for classification of ten different classes of Malignant and Benign skin lesions from color image data. Our key contribution is to focus on the ten most common classes of skin lesions. There are five malignant: Actinic Keratosis (AK), Basal Cell Carcinoma (BCC), Squamous Cell Carcinoma (SCC), Melanoma (MEL), Intraepithelial Carcinoma (IEC) and five benign: Melanocytic Nevus / Mole (ML), Seborrhoeic Keratosis (SK), Dermatofibroma (DF), Haemangioma (VASC), Pyogenic Granuloma (PYO). Moreover, we use only high resolution color images acquired using a standard camera (nondermoscopy). Our image dataset contains 1300 lesions belonging to ten classes (45 AK, 239 BCC, 331 ML, 88 SCC, 257 SK, 76 MEL, 65 DF, 97 VASC, 24 PYO and 78 IEC).

Agriculture has become much more than simply a means to feed ever growing populations. It is very important where in more than 70% population depends on agriculture in India. That means it feeds great number of people. The plant diseases effect the humans directly or indirectly by health or also economically. To detect these plant diseases we need a fast automatic way. Diseases are analyzed by different digital image processing techniques. In this paper, we have done survey on different digital image processing techniques to detect the plant diseases.

The research work deals with an approach to perform texture and morphological based retrieval on a corpus of Basmati rice grain images. The work has been carried out using Image Warping and Image analysis approach. The method has been employed to normalize food grain images and hence eliminating the effects of orientation using image warping technique with proper scaling. The approach has been tested on sufficient number of basmati rice grain images of rice based on intensity, position and orientation. A digital image analysis algorithm based on color, morphological and textural features was developed to identify the six varieties of basmati rice seeds which are widely planted in India. Nine color and nine morphological and textural features were used for discriminant analysis. A back propagation neural network-based classifier was developed to identify the unknown grain types. The color and textural features were presented to the neural network for training purposes. The trained network was then used to identify the unknown grain types. It is also to find the percentage purity of hulled basmati rice grain sample by image processing technique. Commercially the purity test of basmati rice sample is done according to the size of the grain kernel (full, half or broken). By image processing we can also identify any broken basmati rice grains. Here we discuss the various procedures used to obtain the percentage quality of basmati rice grains. Keywords— warping, Image rectification, Image segmentation, Edge Detection, blurring image, Thresholding, Percentage Purity, Pixel area.

Machine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-strictly localized effects that may remain invisible to the conventional analysis with univariate statistical methods. In typical fMRI applications, pattern recognition algorithms "learn" a functional relationship between brain response patterns and a perceptual, cognitive or behavioral state of a subject expressed in terms of a label, which may assume discrete (classification) or continuous (regression) values. This learned functional relationship is then used to predict the unseen labels from a new data set ("brain reading"). In this article, we describe the mathematical foundations of machine learning applications in fMRI. We focus on two methods, support vector machines and relevance vector machines, which are respectively suited for the classification and regression of fMRI patterns. Furthermore, by means of several examples and applications, we illustrate and discuss the methodological challenges of using machine learning algorithms in the context of fMRI data analysis.

India is the agriculture based country, since it contributes 7.68 percent of total global agricultural output. In India, agricultural sector contributes about seventeen percentage of total Indian gross domestic product (GDP). Effective growth and improved yield of plants are necessary for increment of farmer's profit and economy of India. For this purpose farmers need domain experts for manual monitoring of plants. But manual monitoring will not give satisfactory result all the time. Moreover, domain experts are not available at all regions and are expensive as farmers have to pay fees including travelling charges. Hence, it requires developing an efficient smart farming technique which will help for better yield and growth with less human efforts. In this paper, we provide a review on methods developed by various researchers for detection of diseases in plants, in the field of image processing. It includes research in disease detection of plants such as apple, grapes, pepper, pomegranate, tomato etc.

As known, adventure and experience about lesson, entertainment, and knowledge can be gained by only reading a book. Even it is not directly done, you can know more about this life, about the world. We offer you this proper and easy way to gain those all. We offer many book collections from fictions to science at all. One of them is this computer vision algorithms and applications that can be your partner.

Humans detect and identify faces in a scene with little or no effort . However, building an automated system that accomplishes this task is very difficult . There are several related Subproblems : detection of a pattern as a face . identification of the face, analysis of facial expressions, and classification based on physical features of the face . A system that performs these operations will find many applications, e .g . criminal identification, authentication in secure systems, etc . Most of the work to date has been in identification . This paper surveys the past work in solving these problems . The capability of the human visual system with respect to these problems is also discussed . It is meant to serve as a guide for an automated system . Some new approaches to these problems are also briefly discussed . Face detection Face identification Facial expressions Classification Facial features


Question: Identifying fruits and vegetables through the application of machine learning techniques.

The ImageNet project: Constructing a vast, categorized image repository

This document discusses the ImageNet initiative, which aims to create a large-scale, hierarchically organized image database. Unlike previous image databases, ImageNet is designed to encompass a vast array of object categories, making it particularly useful for a wide range of computer vision and machine learning applications, including fruit and vegetable identification. The database is meticulously organized, with images grouped into categories based on the WordNet lexical database, ensuring a comprehensive and systematic coverage of visual concepts. Each category contains hundreds to thousands of images, all of which are human-annotated to ensure accuracy. The sheer scale and diversity of ImageNet make it an invaluable resource for training machine learning models, especially those focused on object recognition tasks such as identifying fruits and vegetables in images. By providing a rich source of labeled data, ImageNet facilitates the development and evaluation of algorithms that can accurately classify fruits and vegetables, contributing to advancements in agricultural technology and food processing automation.

Computer vision-based fire detection involves flame detection and smoke detection. This paper proposes a new flame detection algorithm, which is based on a Bag-of-Features technique in the YUV color space. Inspired by that the color of flame in image and video will fall in certain regions in the color space, models of flame pixels and non-flame pixels are established based on code book in the training phase in our proposal. In the testing phase, the input image is split into some N×N blocks and each block is classified respectively. In each N×N block, the pixels values in the YUV color space are extracted as features, just as in the training phase. According to the experimental results, our proposed method can reduce the number of false alarms greatly compared with an alternative algorithm, while it also ensures the accurate classification of positive samples. The classification performance of our proposed method is better than that of alternative algorithms.

A leaf is an organ of vascular plant and is the principal lateral appendage of the stem. Each leaf has a set of features that differentiate it from the other leaves, such as margin and shape. This work proposes a comparison of supervised plant leaves classification using different approaches, based on different representations of these leaves, and the chosen algorithm. Beginning with the representation of leaves, we presented leaves by a fine-scale margin feature histogram, by a Centroid Contour Distance Curve shape signature, or by an interior texture feature histogram in 64 element vector for each one, after we tried different combination among these features to optimize results. We classified the obtained vectors. Then we evaluate the classification using cross validation. The obtained results are very interesting and show the importance of each feature. The classification of plant leaf images with biometric features. Traditionally, the trained taxonomic perform this process by following various tasks. The taxonomic usually classify the plants based on flowering and associative phenomenon. It was found that this process was time consuming and difficult. The biometric features of plants leaf like venation make this classification easy. Leaf biometric feature are analyzed using computer based method like morphological feature analysis and artificial neural network and Naive bayes based classifier. KNN classification model take input as the leaf venation morphological feature and classify them into four different species. The result of this classification based on leaf venation is achieved 96.53% accuracy in the training of the model for classification of leaves provide the 91% accuracy in testing to classify the leaf images. Keyword: Artificial neural network, K-NN Classification, Leaf venation pattern, Morphological Features

This paper proposed a deep convolutional neural network (DCNN) based framework for large-scale oil palm tree detection using high-resolution remote sensing images in Malaysia. Different from the previous palm tree or tree crown detection studies, the palm trees in our study area are very crowded and their crowns often overlap. Moreover, there are various land cover types in our study area, e.g. impervious, bare land, and other vegetation, etc. The main steps of our proposed method include large-scale and multi-class sample collection, AlexNet-based DCNN training and optimization, sliding window-based label prediction, and post-processing. Compared with the manually interpreted ground truth, our proposed method achieves detection accuracies of 92%–97% in our study area, which are greatly higher than the accuracies obtained from another two detection methods used in this paper.

Deep learning has made significant breakthrough in the past decade. In certain application domain, its detection accuracy has surpassed human being in the same task, e.g., voice recognition and object detection. Various novel applications has been developed and achieved good performance by leveraging the latest advances in deep learning. In this paper, we propose to utilize deep learning based technique, specifically, Convolutional Neural Network (CNN), to develop an auto-counting system for supermarket scenario. Given a picture, the system can automatically detect the specified categories of goods (e.g., Head & Shoulders bottles) and their respective numbers. To improve detection accuracy of the system, we propose to combine hard example mining and multi-scale feature extraction to the Faster R-CNN framework. Experimental results demonstrate the efficacy of the proposed system. Specifically, our system achieves an mAP of 92.1%, which is better than the state-of-the-art, and the response time is about 250 ms per image, including all steps on a GTX 1080 GPU.

Categorization of medical images means selecting the appropriate class for a given image out of a set of pre-defined categories. This is an important step for data mining and content-based image retrieval (CBIR). So far, published approaches are capable to distinguish up to 10 categories. In this paper, we evaluate automatic categorization into more than 80 categories describing the imaging modality and direction as well as the body part and biological system examined. Based on 6231 reference images from hospital routine, 85.5% correctness is obtained combining global texture features with scaled images. With a frequency of 97.7%, the correct class is within the best ten matches, which is sufficient for medical CBIR applications.

Algorithms that produce classifiers with large margins, such as support vector machines (SVMs), AdaBoost, etc. are receiving more and more attention in the literature. This paper presents a real application of SVMs for synthetic aperture radar automatic target recognition (SAR/ATR) and compares the result with conventional classifiers. The SVMs are tested for classification both in closed and open sets (recognition). Experimental results showed that SVMs outperform conventional classifiers in target classification. Moreover, SVMs with the Gaussian kernels are able to form a local " bounded " decision region around each class that presents better rejection to confusers.

Answering questions correctly from standardized eighth-grade science tests is itself a test of machine intelligence.

This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely efficient classifiers[6]. The third contribution is a method for combining increasingly more complex classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. The cascade can be viewed as an object specific focus-of-attention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest. In the domain of face detection the system yields detection rates comparable to the best previous systems. Used in real-time applications, the detector runs at 15 frames per second without resorting to image differencing or skin color detection.

Each and every sector in this digital world is undergoing a dramatic change due to the influence of IT field. The agricultural sector needs more support for its development in developing countries like India. Price prediction helps the farmers and also Government to make effective decision. Based on the complexity of vegetable price prediction, making use of the characteristics of neural networks such as self-adapt, self-study and high fault tolerance, to build up the model of Back-propagation neural network to predict vegetable price. A prediction model was set up by applying the neural network. Taking tomato as an example, the parameters of the model are analyzed through experiment. At the end of the result of Back-propagation neural network shows absolute error percentage of monthly and weekly vegetable price prediction and analyze the accuracy percentage of the price prediction.

Supervised classification is one of the tasks most frequently carried out by so-called Intelligent Systems. Thus, a large number of techniques have been developed based on Artificial Intelligence (Logic-based techniques, Perceptron-based techniques) and Statistics (Bayesian Networks, Instance-based techniques). The goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. The resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known, but the value of the class label is unknown. This paper describes various classification algorithms and the recent attempt for improving classification accuracy—ensembles of classifiers.