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The Internet of Things (IoT) has seen significant growth, enabling connectivity and intelligence in various domains which use RFID communication most. However, this growth has also brought forth significant security challenges, particularly concerning replay attacks, which have troubled previous works. In our study, we introduce an innovative security solution that uses elliptic curve cryptography (ECC) with zero-knowledge proof (ZKP) specifically tailored for RFID-communicated applications. By leveraging ECC with ZKP, we not only improve the security of IoT systems but also reduce the persistent threat of replay attacks. Unlike traditional methods, our approach ensures that sensitive data is securely transmitted and authenticated without the risk of unauthorized duplication. We validated our approach using Scyther and BAN logic, well-known tools for assessing security protocols. These validations confirm the robustness of our solution in addressing security challenges and provide further assurance of its effectiveness in protecting IoT systems against various threats, including replay attacks. Our comprehensive analysis revealed that our approach outperforms existing solutions in terms of communication costs and computation costs. The improved efficiency in these key areas underscores the practicality and viability of our solution, further solidifying its position as a leading option for safeguarding IoT ecosystems against emerging threats. [ABSTRACT FROM AUTHOR]

The diversity and scarcity of the medical information makes it difficult to create precise global classification approach for the healthcare applications. The main motive is the privacy issue that restricts the data exchanging scope between healthcare institutions. On the contrary, an information from single source is not adequate for developing the worldwide diagnosis approach. The Federated Learning (FL) is a promising solution for privacy and data multiplicity issues, an appropriate aggregation model for multi class and dissimilar medical information is still challenging task in the recognition. Moreover, the FL approaches does not effectively analyzes the each participant execution in the local model and secures the user data. In order to overcome this issue, the Zero-Knowledge Proof (ZKP) based FL approach is developed over blockchain (BC) for performing the COVID-19 classification. The global model of FL uses the two layer Long Short Term Memory (2LLSTM) with federated proximal term (FedProx) namely 2LLSTMFP while the Convolutional Neural Network (CNN) is used in the local model. The integration ZKP and BS is used to improve the data confidentiality while the immutability of BC helps to prevent unauthorized variations for the ledger. The developed FLBC-ZKP is analyzed with two datasets such as COVID-19 Radiography, and CXR images pneumonia and COVID-19. The FLBC-ZKP is evaluated using accuracy, recall, precision, specificity, F1-score, False Negative Rate (FNR) and False Positive Rate (FPR). The existing researches such as WMT, MCCF, 3SFDL and TOTL are used to compare the FLBC-ZKP method. The FLBC-ZKP achieves improved accuracy of 98.34 % for COVID-19 Radiography dataset that is better than the MCCF and 3SFDL. [ABSTRACT FROM AUTHOR]

Current authentication schemes based on zero-knowledge proof (ZKP) still face issues such as high computation costs, low efficiency, and security assurance difficulty. Therefore, we propose a secure and efficient authentication scheme (SEAS) for large-scale IoT devices based on ZKP. In the initialization phase, the trusted authority creates prerequisites for device traceability and system security. Then, we propose a new registration method to ensure device anonymity. In the identity tracing and revocation phase, we revoke the real identity of abnormal devices by decrypting and updating group public keys, avoiding their access and reducing revocation costs. In the authentication phase, we check the arithmetic relationship between blind certificates, proofs, and other random data. We propose a new anonymous batch authentication method to effectively reduce computation costs, enhance authentication efficiency, and guarantee device authentication security. Security analysis and experimental results show that an SEAS can ensure security and effectively reduce verification time and energy costs. Its security and performance exceed existing schemes. [ABSTRACT FROM AUTHOR]

In this paper, we propose a smart contract-based multi-candidate self-tallying voting scheme in order to guarantee the privacy of ballots in the case of multiple candidates. This scheme uses the ElGamal cryptosystem to ensure the security of the ballots, and combines it with a Distributed Encryption algorithm to make the voting scheme have self-tallying features, and guarantees the correctness of the intermediate data through zero-knowledge proofs. The experimental results show that the scheme improves the voting efficiency without compromising the security. [ABSTRACT FROM AUTHOR]

Suppose that a sequence of n cards, numbered 1 to n , is placed face up in random order. Let k be the number on the first card in the sequence. Then take the first k cards from the sequence, rearrange that subsequence of k cards in reverse order, and return them to the original sequence. Repeat this prefix reversal until the number on the first card in the sequence becomes 1. This is a one-player card game called Topswops. The computational complexity of Topswops has not been thoroughly investigated. For example, letting f (n) denote the maximum number of prefix reversals for Topswops with n cards, values of f (n) for n ≥ 20 remain unknown. In general, there is no known efficient algorithm for finding an initial sequence of n cards that requires exactly ℓ prefix reversals for any integers n and ℓ . In this paper, using a deck of cards, we propose a physical zero-knowledge proof protocol that allows a prover to convince a verifier that the prover knows an initial sequence of n cards that requires ℓ prefix reversals without leaking knowledge of that sequence. We also deal with Botdrops, a variant of Topswops. [ABSTRACT FROM AUTHOR]

Decomposition puzzles are pencil-and-paper logic puzzles that involve partitioning a rectangular grid into several regions to satisfy certain rules. In this paper, we construct a generic card-based protocol called printing protocol, which can be used to physically verify solutions of decompositon puzzles. We apply the printing protocol to develop card-based zero-knowledge proof protocols for two such puzzles: Five Cells and Meadows. These protocols allow a prover to physically show that he/she knows solutions of the puzzles without revealing them. [ABSTRACT FROM AUTHOR]

UAVs are increasingly being used in various domains, from personal and commercial applications to military operations. Ensuring the security and trustworthiness of UAV communications is crucial, and blockchain technology has been explored as a solution. However, privacy remains a challenge, especially in public blockchains. In this work, we propose a novel approach utilizing zero-knowledge proof techniques, specifically zk-SNARKs, which are non-interactive cryptographic proofs. This approach allows UAVs to prove their authenticity or location without disclosing sensitive information. We generated zk-SNARK proofs using the Zokrates tool on a Raspberry Pi, simulating a drone environment, and analyzed power consumption and CPU utilization. The results are promising, especially in the case of larger drones with higher battery capacities. Ethereum was chosen as the public blockchain platform, with smart contracts developed in Solidity and tested on the Sepolia testnet using Remix IDE. This novel proposed approach paves the way for a new path of research in the UAV area. [ABSTRACT FROM AUTHOR]

The increased use of artificial intelligence generated content (AIGC) among vast user populations has heightened the risk of private data leaks. Effective auditing and regulation remain challenging, further compounding the risks associated with the leaks involving model parameters and user data. Blockchain technology, renowned for its decentralized consensus mechanism and tamper-resistant properties, is emerging as an ideal tool for documenting, auditing, and analyzing the behaviors of all stakeholders in machine learning as a service (MLaaS). This study centers on biometric recognition systems, addressing pressing privacy and security concerns through innovative endeavors. We conducted experiments to analyze six distinct deep neural networks, leveraging a dataset quality metric grounded in the query output space to quantify the value of the transfer datasets. This analysis revealed the impact of imbalanced datasets on training accuracy, thereby bolstering the system's capacity to detect model data thefts. Furthermore, we designed and implemented a novel Bio-Rollup scheme, seamlessly integrating technologies such as certificate authority, blockchain layer two scaling, and zero-knowledge proofs. This innovative scheme facilitates lightweight auditing through Merkle proofs, enhancing efficiency while minimizing blockchain storage requirements. Compared to the baseline approach, Bio-Rollup restores the integrity of the biometric system and simplifies deployment procedures. It effectively prevents unauthorized use through certificate authorization and zero-knowledge proofs, thus safeguarding user privacy and offering a passive defense against model stealing attacks. [ABSTRACT FROM AUTHOR]

Internet of Medical Things (IoMT) has gradually become the main solution for smart healthcare, and cloud-assisted IoMT is becoming a critical computing paradigm to achieve data collection, fine-grained data analysis, and sharing in healthcare domains. Since IoMT data can be frequently shared for accurate diagnosis, prognosis prediction, and health counseling, how to solve the contradiction between data sharing and privacy protection for IoMT data is a challenge problem. Besides, the cloud-assisted medical system is still at risk of a single point of failure and usually suffers from poor scalability and large response delay. Hence, we propose a blockchain-based privacy-preserving and secure sharing scheme for IoMT data, named BAPS. In BAPS, the Interplanetary File System (IPFS) is adopted to store encrypted records. Then, a non-interactive zero-knowledge proof protocol is employed to verify whether the stored data meets the specific request from data requesters without disclosing personal privacy. Moreover, we combine cryptographic primitives and decentralized smart contracts to achieve user anonymity. Finally, we leverage blockchain and proxy re-encryption to achieve fine-grained sharing of healthcare data. Security analysis indicates that this scheme meets the expected security requirements. The computational cost of BAPS is reduced by about 6% compared to state-of-the-art schemes, while the communication overhead is reduced by about 8%. Both theoretical analysis and experiment results show that this scheme can realize privacy-preserving and secure data sharing with acceptable computational and communication costs. [ABSTRACT FROM AUTHOR]

Digital credentials represent digital versions of physical credentials. They are the cornerstone of digital identity on the Internet. In order to enhance privacy, different authors implement selective disclosure in digital credentials, allowing users to disclose only the claims or attributes they want. This paper gives an overview of the most influential articles for selective disclosure, a chronology of the evolution of the methods, and a list of strategies and approaches to the problem. We identify the categories of approaches and their advantages and disadvantages. In addition, we recognize research gaps and open challenges and provide potential future directions.

This study proposes an privacy-protection method for blockchain transactions based on lightweight homomorphic encryption, aiming to ensure the security of transaction data and user privacy, and improve transaction efficiency. We have built a blockchain infrastructure and, based on its structural characteristics, adopted zero-knowledge proof technology to verify the legitimacy of data, ensuring the authenticity and accuracy of transactions from the application end to the smart-contract end. On this basis, the Paillier algorithm is used for key generation, encryption, and decryption, and intelligent protection of blockchain transaction privacy is achieved through a secondary encryption mechanism. The experimental results show that this method performs well in privacy and security protection, with a data leakage probability as low as 2.8%, and can effectively defend against replay attacks and forged-transaction attacks. The degree of confusion remains above 0.9, with small fluctuations and short running time under different key lengths and moderate CPU usage, achieving lightweight homomorphic encryption. This not only ensures the security and privacy of transaction data in blockchain networks, but also reduces computational complexity and resource consumption, better adapting to the high-concurrency and low-latency characteristics of blockchain networks, thereby ensuring the efficiency and real-time performance of transactions. [ABSTRACT FROM AUTHOR]

The need for secure user authentication in blockchain-based applications has been growing with the increased adoption of Decentralized Identity (DID) credentials in blockchain. Zokrates, a tool designed to protect user privacy within smart contracts, had a limitation in that it could not accept authenticated user information such as credentials, only allowing the use of manually inputted data. In this paper, we propose a smart contract system that securely validates DID credentials to overcome the limitations of traditional centralized authentication systems. This system ensures the safe identification of users within blockchain-based applications by authenticating their identities in a trusted manner within the blockchain. As the demand for user authentication in blockchain rises, this paper emphasizes the significance of a blockchain-based identity verification system that guarantees both privacy and security. Leveraging the Zero-Knowledge Proof method and utilizing the Zokrates tool, this innovative approach aims to provide solutions for the digital identity verification process, thereby expanding the scope of blockchain technology applications. Moreover, we also provide a CLI for each entity. We help anyone who wants to authenticate their identity using the tool to safely verify it on-chain. [ABSTRACT FROM AUTHOR]

In this study, a new data-sharing method is proposed that uses a private InterPlanetary File System—a decentralized storage system operated within a closed network—to distribute data to external entities while making its authenticity verifiable. Among the two operational modes of IPFS, public and private, this study focuses on the method for using private IPFS. Private IPFS is not open to the general public; although it poses a risk of data tampering when distributing data to external parties, the proposed method ensures the authenticity of the received data. In particular, this method applies a type of zero-knowledge proof, namely, the Groth16 protocol of zk-SNARKs, to ensure that the data corresponds to the content identifier in a private IPFS. Moreover, the recipient's name is embedded into the distributed data to prevent unauthorized secondary distribution. Experiments confirmed the effectiveness of the proposed method for an image data size of up to 120 × 120 pixels. In future studies, the proposed method will be applied to larger and more diverse data types. [ABSTRACT FROM AUTHOR]

Authentication is a crucial security service on the Internet. In real-world applications, multiple independent trust domains often exist, with each recognizing only certain identities within their own systems. During cross-domain access, users cannot directly use their original certificates, which presents a cross-domain authentication problem. Traditional centralized schemes typically employ a trusted third party (TTP) to facilitate the transfer of identity trust across domains. These schemes inevitably inherit the vulnerabilities associated with single points of failure. In contrast, blockchain-based decentralized schemes effectively eliminate the potential threats posed by TTPs. However, the openness and transparency of the blockchain also bring new security issues, such as privacy leakage. In this paper, we propose a zk-SNARK-based anonymous scheme on the blockchain for cross-domain authentication. Specifically, our scheme adopts an authorization-then-proof structure, which strikes a delicate balance between anonymity and revocability. We provide theoretical proofs for the security of our scheme and explain how it achieves proactive revocability. Experimental evaluation results demonstrated that our scheme is both secure and efficient, and the revocation could be accomplished by introducing only 64 bytes of on-chain storage with one hash comparison. [ABSTRACT FROM AUTHOR]

The Internet of Vehicles (IoV) is extensively deployed in outdoor and open environments to effectively address traffic efficiency and safety issues by connecting vehicles to the network. However, due to the open and variable nature of its network topology, vehicles frequently engage in cross-domain interactions. During such processes, directly uploading sensitive information to roadside units for interaction may expose it to malicious tampering or interception by attackers, thus compromising the security of the cross-domain authentication process. Additionally, IoV imposes high real-time requirements, and existing cross-domain authentication schemes for IoV often encounter efficiency issues. To mitigate these challenges, we propose CAIoV, a blockchain-based efficient cross-domain authentication scheme for IoV. This scheme comprehensively integrates technologies such as zero-knowledge proofs, smart contracts, and Merkle hash tree structures. It divides the cross-domain process into anonymous cross-domain authentication and safe cross-domain authentication phases to ensure efficiency while maintaining a balance between efficiency and security. Finally, we evaluate the performance of CAIoV. Experimental results demonstrate that our proposed scheme reduces computational overhead by approximately 20%, communication overhead by around 10%, and storage overhead by nearly 30%. [ABSTRACT FROM AUTHOR]

This study examines the issues of privacy protection, data security, and query efficiency in blockchain-based electronic medical record (EMR) sharing. It proposes a secure storage and sharing scheme for EMR based on Hyperledger Fabric and the InterPlanetary File System (IPFS). To mitigate the privacy risks of data mining that could reveal patient identities, we establish an attribution channel in Hyperledger Fabric to store EMR ownership information and a data channel to store the storage location, digest, and usage records of medical data. Encrypted medical data are stored in the IPFS. To improve query efficiency in the blockchain, we integrate queryable medical data attributes into a composite key for conditional queries, avoiding complex data filtering processes. Additionally, we use a zero-knowledge proof combined with smart contracts for decentralized identity verification, eliminating reliance on third-party centralized verification services and enhancing system security. We also integrate AES and proxy re-encryption techniques to ensure data security during sharing. This scheme provides a more secure, efficient, and privacy-preserving approach for EMR systems, with significant practical implications and broad application potential. [ABSTRACT FROM AUTHOR]

The Internet of Things and blockchain, as important technology foundations to enable Web 3.0, have greatly impacted the living and production patterns of people. When they meet together, blockchain services can be used to solve some IoT challenges that traditional information technologies cannot solve. Therefore, how to use blockchain services in IoT environment, and what key security challenges in IoT environment can be addressed by blockchain services are two critical issues. In this article, we propose a novel hybrid IoT architecture based on blockchain services to solve the first issue. To solve the second issue, we analyze IoT security threats and challenges, and propose blockchain-based security solutions, which are illustrated with the application cases of edge computing and fog computing based on smart contracts. Then we design and implement blockchain-based IoT zero-knowledge proof authentication experiments using the web3.js library to demonstrate the lightweight blockchain services performance in IoT application scenarios. [ABSTRACT FROM AUTHOR]

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The increased use of artificial intelligence generated content (AIGC) among vast user populations has heightened the risk of private data leaks. Effective auditing and regulation remain challenging, further compounding the risks associated with the leaks involving model parameters and user data. Blockchain technology, renowned for its decentralized consensus mechanism and tamper-resistant properties, is emerging as an ideal tool for documenting, auditing, and analyzing the behaviors of all stakeholders in machine learning as a service (MLaaS). This study centers on biometric recognition systems, addressing pressing privacy and security concerns through innovative endeavors. We conducted experiments to analyze six distinct deep neural networks, leveraging a dataset quality metric grounded in the query output space to quantify the value of the transfer datasets. This analysis revealed the impact of imbalanced datasets on training accuracy, thereby bolstering the system’s capacity to detect model data thefts. Furthermore, we designed and implemented a novel Bio-Rollup scheme, seamlessly integrating technologies such as certificate authority, blockchain layer two scaling, and zero-knowledge proofs. This innovative scheme facilitates lightweight auditing through Merkle proofs, enhancing efficiency while minimizing blockchain storage requirements. Compared to the baseline approach, Bio-Rollup restores the integrity of the biometric system and simplifies deployment procedures. It effectively prevents unauthorized use through certificate authorization and zero-knowledge proofs, thus safeguarding user privacy and offering a passive defense against model stealing attacks.

Zero-knowledge proof (ZKP) is a cryptographic primitive that enables a prover to convince a verifier that a statement is true, without revealing any other information beyond the correctness of the statement itself. Due to its powerful capabilities, its most practical type, called zero-knowledge Succinct Non-interactive ARgument of Knowledge (zkSNARK), has been widely deployed in various privacypreserving applications such as cryptocurrencies and verifiable computation. Although state-of-the-art zkSNARKs are highly efficient for the verifier, the computational overhead for the prover is still orders of magnitude too high to warrant use in many applications. This overhead arises from several time-consuming operations, including large-scale matrix-vector multiplication (MUL), number-theoretic transform (NTT), and especially the multi-scalar multiplication (MSM) which constitutes the largest proportion. Therefore, further efficiency improvements are needed. In this paper, we focus on comprehensive optimization of running time and storage space required by the MSM algorithm on GPUs. Specifically, we propose a novel, modular and adaptive parameter configuration technique—elastic MSM to enable us to adjust the scale of MSM according to our own wishes by performing a corresponding amount of preprocessing. This technique enables us to fully unleash the potential of various efficient parallel MSM algorithms. We have implemented and tested elastic MSM over three prevailing parallel Pippenger algorithms on GPUs. Across various preprocessing space limitations (across various MSM scales), our constructions achieve up to about 1.90×, 1.08× and 1.36× (2.58×, 1.39× and 1.91×) speedup versus three state-of-the-art parallel Pippenger algorithms on GPUs, respectively. From another perspective, elastic MSM could also be regarded as a preprocessing technique over the well-known Pippenger algorithm, which is modular and could be used to accelerate almost all the most advanced parallel Pippenger algorithms on GPUs. Meanwhile, elastic MSM provides an adaptive trade-off between the running time and the extra storage space needed by parallel Pippenger algorithms on GPUs. This is the first preprocessing technique to retain the improved MSM computation brought by preprocessing under varying storage space limitations. Specifically, across various preprocessing space limitations (across various MSM scales), our constructions achieve up to about 192× and 223× (159× and 174×) speedup versus two state-ofthe- art preprocessing parallel Pippenger algorithms on GPUs, respectively.

Abstract There are a series of challenges in microgrid transactions, and blockchain technology holds the promise of addressing these challenges. However, with the increasing number of users in microgrid transactions, existing blockchain systems may struggle to meet the growing demands for transactions. Therefore, this paper proposes an efficient and secure blockchain consensus algorithm designed to meet the demands of large-scale microgrid electricity transactions. The algorithm begins by utilizing a Spectral clustering algorithm to partition the blockchain network into different lower-level consensus set based on the transaction characteristics of nodes. Subsequently, a dual-layer consensus process is employed to enhance the efficiency of consensus. Additionally, we have designed a secure consensus set leader election strategy to promptly identify leaders with excellent performance. Finally, we have introduced an authentication method that combines zero-knowledge proofs and key sharing to further mitigate the risk of malicious nodes participating in the consensus. Theoretical analysis indicates that our proposed consensus algorithm, incorporating multiple layers of security measures, effectively withstands blockchain attacks such as denial of service. Simulation experiment results demonstrate that our algorithm outperforms similar blockchain algorithms significantly in terms of communication overhead, consensus latency, and throughput.

With the continuous advancement of information technology, a growing number of works, including articles, paintings, and music, are being digitized. Digital content can be swiftly shared and disseminated via the Internet. However, it is also vulnerable to malicious plagiarism, which can seriously infringe upon the rights of creators and dampen their enthusiasm. To protect creators' rights and interests, a sophisticated method is necessary to authenticate digital intellectual property rights. Traditional authentication methods rely on centralized, trustworthy organizations that are susceptible to single points of failure. Additionally, these methods are prone to network attacks that can lead to data loss, tampering, or leakage. Moreover, the circulation of copyright information often lacks transparency and traceability in traditional systems, which leads to information asymmetry and prevents creators from controlling the use and protection of their personal information during the authentication process. Blockchain technology, with its decentralized, tamper-proof, and traceable attributes, addresses these issues perfectly. In blockchain technology, each node is a peer, ensuring the symmetry of information. However, the transparent feature of blockchains can lead to the leakage of user privacy data. Therefore, this study designs and implements an Ethereum blockchain-based intellectual property authentication scheme with privacy protection. Firstly, we propose a method that combines elliptic curve cryptography (ECC) encryption with digital signatures to achieve selective encryption of user personal information. Subsequently, an authentication algorithm based on Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK) is adopted to complete the authentication of intellectual property ownership while encrypting personal privacy data. Finally, we adopt the InterPlanetary File System (IPFS) to store large files, solving the problem of blockchain storage space limitations. [ABSTRACT FROM AUTHOR]

In this paper, we propose a physical protocol to verify the first nonzero term of a sequence using a deck of cards. The protocol lets a prover show the value of the first nonzero term of a given sequence to a verifier without revealing which term it is. Our protocol uses Θ (1) shuffles, which is asymptotically lower than that of an existing protocol of Fukusawa and Manabe which uses Θ (n) shuffles, where n is the length of the sequence. We also apply our protocol to construct zero-knowledge proof protocols for three well-known logic puzzles: ABC End View, Goishi Hiroi, and Toichika. These protocols enable a prover to physically show that he/she know solutions of the puzzles without revealing them. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to study the following two issues regarding blockchain crowdsourcing. First, to design smart contracts with lower consumption to meet the needs of blockchain crowdsourcing services and also need to design better interaction modes to further reduce the cost of blockchain crowdsourcing services. Second, to design an effective privacy protection mechanism to protect user privacy while still providing high-quality crowdsourcing services for location-sensitive multiskilled mobile space crowdsourcing scenarios and blockchain exposure issues. Design/methodology/approach: This paper proposes a blockchain-based privacy-preserving crowdsourcing model for multiskill mobile spaces. The model in this paper uses the zero-knowledge proof method to make the requester believe that the user is within a certain location without the user providing specific location information, thereby protecting the user's location information and other privacy. In addition, through off-chain calculation and on-chain verification methods, gas consumption is also optimized. Findings: This study deployed the model on Ethereum for testing. This study found that the privacy protection is feasible and the gas optimization is obvious. Originality/value: This study designed a mobile space crowdsourcing based on a zero-knowledge proof privacy protection mechanism and optimized gas consumption. [ABSTRACT FROM AUTHOR]

There are a series of challenges in microgrid transactions, and blockchain technology holds the promise of addressing these challenges. However, with the increasing number of users in microgrid transactions, existing blockchain systems may struggle to meet the growing demands for transactions. Therefore, this paper proposes an efficient and secure blockchain consensus algorithm designed to meet the demands of large-scale microgrid electricity transactions. The algorithm begins by utilizing a Spectral clustering algorithm to partition the blockchain network into different lower-level consensus set based on the transaction characteristics of nodes. Subsequently, a dual-layer consensus process is employed to enhance the efficiency of consensus. Additionally, we have designed a secure consensus set leader election strategy to promptly identify leaders with excellent performance. Finally, we have introduced an authentication method that combines zero-knowledge proofs and key sharing to further mitigate the risk of malicious nodes participating in the consensus. Theoretical analysis indicates that our proposed consensus algorithm, incorporating multiple layers of security measures, effectively withstands blockchain attacks such as denial of service. Simulation experiment results demonstrate that our algorithm outperforms similar blockchain algorithms significantly in terms of communication overhead, consensus latency, and throughput. [ABSTRACT FROM AUTHOR]

Big data refers to sets of structured, semi-structured, and unstructured data collected by organizations and may be difficult to process using traditional processing methods. Big data pricing is challenging due to uncertainty about its value. Auctions are effective strategies for allocating goods to the bidder with the highest valuation and a mechanism for protecting the interests of both data sellers and market participants. However, most existing data auctions are centralized, making it difficult to establish trust and transparency among sellers, buyers, and auctioneers. Moreover, online environments suffer from the lack of transparency and verifiability, which can lead to collusion attacks in data auctions. To address these challenges, we leverage blockchain technology and propose an anti-collusion smart contract-based data auction system. Since blockchain data transparency often compromises privacy, we leverage cryptographic techniques to enhance privacy, integrity, fairness, correctness, and non-repudiation. To ensure that all participants benefit from the auction, an auction model that considers rationality and budget balance is developed. The Ethereum blockchain is used to demonstrate a proof-of-concept implementation. Analysis has shown that the approach could meet efficiency, security, and privacy requirements. [ABSTRACT FROM AUTHOR]

In most threshold Elliptic Curve Digital Signature Algorithm (ECDSA) signatures using additively homomorphic encryption, the zero-knowledge (ZK) proofs related to the ciphertext or the message space are the bottleneck in terms of bandwidth as well as computation time. In this paper, we propose a compact ZK proof for relations related to the Castagnos–Laguillaumie (CL) encryption, which is 33% shorter and 29% faster than the existing work in PKC 2021. We also give new ZK proofs for relations related to homomorphic operations over the CL ciphertext. These new ZK proofs are useful to construct a bandwidth-efficient universal composable-secure threshold ECDSA without compromising the proactive security and the non-interactivity. In particular, we lowered the communication and computation cost of the key refresh algorithm in the Paillier-based counterpart from |$O(n^3)$| to |$O(n^2)$|⁠. Considering a 5-signer setting, the bandwidth is better than the Paillier-based counterpart for up to 99, 95 and 35% for key generation, key refreshment and pre-signing, respectively. [ABSTRACT FROM AUTHOR]

In virtual power plants, diverse business scenarios involving user data, such as queries, transactions, and sharing, pose significant privacy risks. Traditional attribute-based encryption (ABE) methods, while supporting fine-grained access, fall short of fully protecting user privacy as they require attribute input, leading to potential data leaks. Addressing these limitations, our research introduces a novel privacy protection scheme using zero-knowledge proof and distributed attribute-based encryption (DABE). This method innovatively employs Merkel trees for aggregating user attributes and constructing commitments for zero-knowledge proof verification, ensuring that user attributes and access policies remain confidential. Our solution not only enhances privacy but also fortifies security against man-in-the-middle and replay attacks, offering attribute indistinguishability and tamper resistance. A comparative performance analysis demonstrates that our approach outperforms existing methods in efficiency, reducing time, cost, and space requirements. These advancements mark a significant step forward in ensuring robust user privacy and data security in virtual power plants. [ABSTRACT FROM AUTHOR]

Internet of Vehicles (IoV) has become an indispensable technology to bridge vehicles, persons, and infrastructures and is promising to make our cities smarter and more connected. It enables vehicles to exchange vehicular data (e.g., GPS, sensors, and brakes) with different entities nearby. However, sharing these vehicular data over the air raises concerns about identity privacy leakage. Besides, the centralized architecture adopted in existing IoV systems is fragile to single point-of-failure and malicious attacks. With the emergence of blockchain technology, there is the chance to solve these problems due to its features of being tamper-proof, traceability, and decentralization. In this article, we propose a privacy-preserving vehicular data sharing framework based on blockchain. In particular, we design an anonymous and auditable data sharing scheme using Zero-Knowledge Proof (ZKP) technology so as to protect the identity privacy of vehicles while preserving the vehicular data auditability for Trusted Authorities (TAs). In response to high mobility of vehicles, we design an efficient multi-sharding protocol to decrease blockchain communication costs without compromising the blockchain security. We implement a prototype of our framework and conduct extensive experiments and simulations on it. Evaluation and analysis results indicate that our framework can not only strengthen system security and data privacy but also reduce communication complexity by \(O(\frac{n\sqrt {m}}{m^2})\) times compared to existing sharding protocols. [ABSTRACT FROM AUTHOR]

Abstract In the evolving landscape of global education, the significance of inclusivity and equity has never been more important. Emphasizing the United Nation Sustainable Development Goal 4, this paper explores the innovative application of blockchain-powered Zero-Knowledge Proofs (ZKPs) technology in education, with a particular focus on disability inclusion. This study introduces a novel disability management system powered by Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK). This advanced system enables educational institutions to verify the status of students with disabilities without compromising their personal information, thereby preserving their privacy and reinforcing their identity. This paper evaluates the potential operational efficiency of this prototype system against the existing costs incurred by higher education institutions in disability schemes. It also examines the system’s potential to enhance self-disclosure among students with disability, which is pivotal for their academic success. By advocating for privacy and inclusivity, this study highlights the transformative potential of ZKP in creating an educational environment where students with disabilities can comfortably disclose their needs. This approach not only protects their confidentiality but also empowers them academically, aligning with the global commitment to accessible and inclusive education.

Authentication in low-latency Internet of Things (IoT) networks must satisfy three requirements, namely, high security and privacy preservation, high scalability, and low authentication time. These requirements arise because devices in IoT networks must operate in a secure and scalable manner despite being limited in computational resources. Existing authentication mechanisms focus on the security and privacy of IoT networks but neglect the importance of scalability and authentication time. Therefore, existing authentication mechanisms are unscalable and unsuited to low-latency IoT networks. With a focus on increasing scalability and reducing the authentication time while providing high security and privacy preservation in low-latency IoT networks, we propose a mutual authentication mechanism called Zero-Knowledge Proof-based Privacy-Preserving Mutual Authentication (Z-PMA) for IoT networks. The Z-PMA mechanism utilizes a combination of a zero-knowledge proof, an incentive mechanism, and a permissioned blockchain to provide secure, privacy-preserving, scalable, low-latency authentication for IoT networks. We develop a new approach to address the trade-off between the three requirements for authentication mechanisms for low-latency IoT networks that has the potential to improve the overall performance of these networks. A permissioned blockchain is incorporated in the approach to provide secure and immutable data storage using its distributed and unforgeable ledger. Our experimental results show that the Z-PMA mechanism reduces authentication time than existing state-of-the-art authentication mechanisms, while providing high security and privacy preservation as well as high scalability.

In this paper, we propose a smart contract-based multi-candidate self-tallying voting scheme in order to guarantee the privacy of ballots in the case of multiple candidates. This scheme uses the ElGamal cryptosystem to ensure the security of the ballots, and combines it with a Distributed Encryption algorithm to make the voting scheme have self-tallying features, and guarantees the correctness of the intermediate data through zero-knowledge proofs. The experimental results show that the scheme improves the voting efficiency without compromising the security.

UAVs are increasingly being used in various domains, from personal and commercial applications to military operations. Ensuring the security and trustworthiness of UAV communications is crucial, and blockchain technology has been explored as a solution. However, privacy remains a challenge, especially in public blockchains. In this work, we propose a novel approach utilizing zero-knowledge proof techniques, specifically zk-SNARKs, which are non-interactive cryptographic proofs. This approach allows UAVs to prove their authenticity or location without disclosing sensitive information. We generated zk-SNARK proofs using the Zokrates tool on a Raspberry Pi, simulating a drone environment, and analyzed power consumption and CPU utilization. The results are promising, especially in the case of larger drones with higher battery capacities. Ethereum was chosen as the public blockchain platform, with smart contracts developed in Solidity and tested on the Sepolia testnet using Remix IDE. This novel proposed approach paves the way for a new path of research in the UAV area.

The increased internet usage after the pandemic led the UN Forum to improve cybersecurity measures, with zero-knowledge proofs (ZKP) being a viable solution for securing confidential information. ZKP protocols can be demonstrated through the binary puzzle, an NP-complete logic puzzle with four specific constraints. The key contribution of this paper is its successful implementation of the genetic algorithm as a new method to solve the binary puzzle. The optimized fitness function determined the solution at an average of 1.33-2.33 generations for populations ranging from 100 to 500. Its quadratic property calculated the solution faster than the ordinary linear fitness function. [ABSTRACT FROM AUTHOR]