Pseudorandom Correlation Functions (PCFs) allow two parties, given correlated evaluation keys, to locally generate arbitrarily many pseudorandom correlated strings, e.g. Oblivious Transfer (OT) correlations, which can then be used by the two parties to jointly run secure computation protocols. In this work, we provide a novel and simple approach for constructing PCFs for OT correlation, by relying on constrained pseudorandom functions for a class of constraints containing a weak pseudorandom function (wPRF). We then show that tweaking the Naor-Reingold pseudorandom function and relying on low-complexity pseudorandom functions allow us to instantiate our paradigm. We further extend our ideas to obtain efficient public-key PCFs, which allow the distribution of correlated keys between parties to be non-interactive: each party can generate a pair of public/secret keys, and any pair of parties can locally derive their correlated evaluation key by combining their secret key with the other partyâs public key. In addition to these theoretical contributions, we detail various optimizations and provide concrete instantiations of our paradigm relying on the Boneh-Ishai-PasselĂšgue-Sahai-Wu wPRF and the Goldreich-Applebaum-Raykov wPRF. Putting everything together, we obtain public-key PCFs with a throughput of 15kâ40k OT/s, which is of a similar order of magnitude to the state-of-the-art interactive PCFs and about 4 orders of magnitude faster than state-of-the art public-key PCFs. As a side result, we also show that public-key PCFs can serve as a building block to construct reusable designated-verifier non-interactive zero-knowledge proofs (DV-NIZK) for NP. Combined with our instantiations, this yields simple and efficient reusable DV-NIZKs for NP in pairing-free groups.
Traditional scholarship management systems are often marred by inefficiencies, a lack of transparency, and significant concerns regarding data privacy. These challenges hinder the equitable distribution of educational funds and obscure the scholarship allocation process. This study aims to address these issues by proposing a blockchain-based scholarship management platform. However, scalability issues are introduced along with blockchain-based solutions, for which zk-rollup offers a promising solution. The integration of Self-Sovereign Identity Zero-Knowledge Proof assures secure and private submission of scholarship applications, safeguarding student data while maintaining transparency in the verification process. Additionally, a machine learning model is employed to predict scholarship qualification, functioning as a recommendation system that identifies and prioritizes deserving students. This data-driven approach proactively eliminates barriers that potentially impede deserving students from accessing financial aid, such as administrative oversights or a lack of self-assurance in their qualifications. Our experimental findings confirm the effectiveness of zk-rollups in enhancing transaction efficiency, demonstrating a reduction in transaction confirmation time by approximately 63.6% and a decrease in transaction costs by nearly 90%. Besides, the machine learning model achieved a good performance rating, achieving a balanced accuracy of 86.75% and a mean average precision of 91.68% on a realistically imbalanced test set, reflecting real-world conditions.
Post hoc privacy auditing techniques can be used to test the privacy guarantees of a model, but come with several limitations: (i) they can only establish lower bounds on the privacy loss, (ii) the intermediate model updates and some data must beshared with the auditor to get a better approximation of the privacy loss, and (iii) the auditor typically faces a steep computational cost to run a large number of attacks. In this paper, we propose to proactively generate a cryptographic certificate of privacy during training to forego such auditing limitations. We introduce Confidential-DPproof , a framework for Confidential Proof of Differentially Private Training, which enhances training with a certificate of the (Δ, ÎŽ)-DP guarantee achieved. To obtain this certificate without revealing information about the training data or model, we design a customized zero-knowledge proof protocol tailored to the requirements introduced by differentially private training, including random noise addition and privacy amplification by subsampling. In experiments on CIFAR-10, Confidential-DPproof trains a model achieving state-of-the-art 91% test accuracy with a certified privacy guarantee of (Δ = 0.55, ÎŽ = 10â5)-DP in approximately 100 hours.
Open access
Law, Economics, and Judicial Systems
Occupational and Professional Licensing Regulation
Hengchang Pan, Y. W. Wang, Wei Wang, Ping Cao · 6 authors
Efficient and trusted regulation of unmanned aerial vehicles (UAVs) is an essential but challenging issue in the future era of the Internet of Low-altitude Intelligence, due to the difficulties in UAVsâ identity recognition and location matching, potential for falsified information reporting, etc. To address this challenging issue, in this paper, we propose a blockchain-based UAV location authentication scheme, which employs a distance bounding protocol to establish a location proof, ensuring the authenticity of UAV positions. To preserve the privacy of UAVs, anonymous certificates and zero-knowledge proof are used. The security of the proposed scheme is analyzed. Experiments demonstrate the efficiency and feasibility of the proposed scheme.
The growing complexities in supply chain management, driven by internationalization, decentralization, and modernization, are increasingly threatened by data breaches, network intrusions, and software vulnerabilities. Business stakeholders are turning to blockchain technology as a possible solution to improve modern supply chainsâ reliability, integration, and visibility. Blockchain provides a robust framework that supports product traceability, seamless system integration, and enhanced transparency across operations. However, despite its transformative potential, integrating blockchain into supply chain management (SCM) introduces new security risks. These include threats to the confidentiality of blockchain transactions, as attackers may employ techniques like transaction linkage and deanonymization. This paper explores the specific security challenges related to blockchain-based SCM systems, including risks related to SCM, Internet of Things (IoT) system attacks, and blockchain system vulnerabilities. Additionally, the paper presents a case study on securing the drug supply chain through blockchain technology, focusing on attribute-based identity management and zero-knowledge proofs (ZKP) to bolster security and transparency. This approach highlights how blockchain can enhance data privacy, improve access control and identity management, protect information integrity and traceability, and reduce the risk of counterfeit products throughout the supply chain. The paper provides valuable guidance and outlines potential directions for future research in blockchain-driven security and associated issues for supply chain networks, offering a clear roadmap for researchers.
Alberto Ballesteros-RodrĂguez, Salvador SĂĄnchezâAlonso, MiguelâĂngel Sicilia
The widespread integration of on-demand services founded on proprietary algorithms into various software applications has ushered into a new era of advanced service capabilities. However, using these services entails disclosing information by the customer, not only during the payment process but also when using the service, where certain personal information must be shared to obtain a more personalized service. This practice potentially exposes users to increased security risks in case of data security breaches. In this paper, we introduce a novel framework aimed at enhancing client privacy and ensuring service integrity within the context of computing services that rely on proprietary algorithms. A blockchain-based approach is proposed to enhance user privacy throughout service provision, encompassing both the payment process and the verification of the provided service. Our proposal leverages properties of distributed ledger networks to improve user privacy during payment transactions and incorporates a verification system using zero-knowledge proofs on blockchain to validate the integrity of the contracted service. Finally, we analyze the privacy, overhead, and performance aspects of the framework, employing custom proprietary algorithms. We illustrate this through examples of Convolutional Neural Networks with multiple layers, undisclosed to the client. This emphasizes the potential benefits of its applicability for both service providers and clients.
All of us know that cryptography is an innovative security strategy. Network security construction with authentication techniques of its layers through blockchain technologies is an important field to discuss. The problem that we are trying to solve is the Transaction Privacy Leakage in public Blockchain networks with IOT networks, which has resulted in the publicity of this data on the network as well as synchronizing the information that allowed it to be accessed and propagated between distributed nodes. At the same time, there are some privacy risk concerns associated with public data wherein transactions contain sensitive information about their issuers. Although some previous research introduced models to deal with the problem of Transaction Privacy Leakage like deterministic key generation, mixing services, ring signature, zero-knowledge proof, and quantum-resistant algorithms, the suggested models do not fully achieve prevention or integrity in all cases. This paper presents a developed Multi-Layer Blockchain Security Model (MLBSM) that can be used to protect IoT networks while also facilitating their implementation for protecting IoT networks and similar networks to prevent Transaction Privacy Leakage for all users in the public blockchain network. The clustering concept is utilized to facilitate the multi-layer architecture. By implementing this, we may achieve unprecedented levels of security and transparency in the blockchain network which will protect the privacy of all users in different technologies.
This paper presents a Blockchain-based framework for providing Blockchain services for purposes of stability in terms of consensus protocol infrastructure and governance mechanisms and accessible auxiliary services suitable for the vast majority of current business needs, including fundamental factors such as digital identity with autonomous identity, building solutions to ensure transaction privacy with zero-knowledge proofs, and other services related to digital assets. The proposed framework helps promote digital transformation for businesses, especially small and medium enterprises with limited resources and costs, to apply Blockchain technology to their business models, increasing competitive advantages and assisting the companies in focusing on business logic while still using Blockchain technology in their functions.
Francesca Stabile, Walter LĂșcia, Amr Youssef, Giuseppe FranzĂš
The proliferation of cloud computing technologies has paved the way for deploying networked encrypted control systems, offering high performance, remote accessibility and privacy. However, in scenarios where the control algorithms run on third-party cloud service providers, the controlâs logic might be changed by a malicious agent on the cloud. Consequently, it is imperative to verify the correctness of the control signals received from the cloud. Traditional verification methods, like zero-knowledge proof techniques, are computationally demanding in both proof generation and verification, may require several rounds of interactions between the prover and verifier and, consequently, are inapplicable in real-time control system applications. In this paper, we present a novel computationally inexpensive verifiable computing solution inspired by the probabilistic cut-and-choose approach. The proposed scheme allows the plantâs actuator to validate the computations accomplished by the encrypted cloud-based networked controller without compromising the control schemeâs performance. We showcase the effectiveness and real-time applicability of the proposed verifiable computation scheme using a remotely controlled Khepera-IV differential-drive robot.
Digital credentials represent crucial elements of digital identity on the Internet. Credentials should have specific properties that allow them to achieve privacy-preserving capabilities. One of these properties is selective disclosure, which allows users to disclose only the claims or attributes they must. This paper presents a novel approach to selective disclosure BLS-MT-ZKP that combines existing cryptographic primitives: Boneh-Lynn-Shacham (BLS) signatures, Merkle hash trees (MT) and zero-knowledge proof (ZKP) method called Bulletproofs. Combining these methods, we achieve selective disclosure of claims while conforming to selective disclosure requirements. New requirements are defined based on the definition of selective disclosure and privacy spectrum. Besides selective disclosure, specific use cases for equating digital credentials with paper credentials are achieved. The proposed approach was compared to the existing solutions, and its security, threat, performance and limitation analysis was done. For validation, a proof-of-concept was implemented, and the execution time was measured to demonstrate the practicality and efficiency of the approach.
Genetic research has significantly advanced with the utilization of DNA Short Tandem Repeat (STR) profiling, playing a pivotal role in forensic investigations and medical studies.However, the surge in genetic data usage has sparked concerns about the privacy and security of individuals' genetic information.In response to these challenges, this paper introduces a pioneering framework that integrates zero-knowledge proofs and blockchain technology to enhance the security of DNA STR profiles.The primary objective of this study is to establish a secure and privacypreserving environment for the comprehensive management of DNA STR profiles.The proposed framework combines the cryptographic guarantees of zero-knowledge proofs with the decentralized and tamper-resistant nature of blockchain technology to enable secure transactions involving DNA STR profiles.This cryptographic technique allows parties to validate the authenticity of information without exposing the actual data, ensuring privacy during data interactions.Blockchain technology is leveraged for the creation of a decentralized and distributed ledger that stores DNA STR profiles for tamper proofing.Smart contracts play a crucial role in enforcing security policies within the blockchain network.These contracts automate the execution of predefined rules, such as access control and data sharing permissions.Additionally, a consensus mechanism is implemented within the blockchain network to ensure agreement among network participants on the validity of transactions.A detailed pseudo algorithm is introduced in the research paper, outlining the step-by-step processes involved in securing DNA STR profiles using zero-knowledge proofs and blockchain.The proposed framework not only addresses current challenges in genetic privacy but also establishes a foundation for the future development of secure genetic databases.
Data is regarded as a valuable asset, and sharing data is a prerequisite for fully exploiting the value of data. However, the current medical data sharing scheme lacks a fair incentive mechanism, and the authenticity of data cannot be guaranteed, resulting in low enthusiasm of participants. A fair and trusted medical data trading scheme based on smart contracts is proposed, which aims to encourage participants to be honest and improve their enthusiasm for participation. The scheme uses zero-knowledge range proof for trusted verification, verifies the authenticity of the patientâs data and the specific attributes of the data before the transaction, and realizes privacy protection. At the same time, the game pricing strategy selects the best revenue strategy for all parties involved and realizes the fairness and incentive of the transaction price. The smart contract is used to complete the verification and game bargaining process, and the blockchain is used as a distributed ledger to record the medical data transaction process to prevent data tampering and transaction denial. Finally, by deploying smart contracts on the Ethereum test network and conducting experiments and theoretical calculations, it is proved that the transaction scheme achieves trusted verification and fair bargaining while ensuring privacy protection in a decentralized environment. The experimental results show that the model improves the credibility and fairness of medical data transactions, maximizes social benefits, encourages more patients and medical institutions to participate in the circulation of medical data, and more fully taps the potential value of medical data.
Daniel Commey, Bin Mai, Sena Hounsinou, Garth V. Crosby
This paper reviews the role of blockchain technology in enhancing the security of Internet of Things (IoT) systems and maintaining data integrity. We address the increased vulnerabilities and broader attack surface resulting from the integration of blockchain and IoT. The review emphasizes the potential of technologies like zero-knowledge proofs (ZKP) and post-quantum cryptography (PQC) to mitigate these security challenges. Additionally, we explore how game theory, machine learning, and cyber deception strengthen the defense of blockchain-based IoT systems against various threats. The paper also identifies open research areas, emphasizing the need for continued exploration to advance these fields. An additional contribution of this study is introducing a conceptual framework incorporating these technologies, laying the groundwork for developing advanced security solutions within the blockchain-enhanced IoT ecosystem.
Zero Knowledge Proofs (ZKPs) have emerged as transformative cryptographic primitives, enabling a wide variety of modern privacy-preserving applications across various domains. This paper presents substantial advancements in contemporary ZKPs, exploring the latest trends in ZKP schemes, novel applications, and scalable ZKP-based systems. We provide an in-depth analysis of state-of-the-art ZKP structures, including zk-SNARKs, zk-STARKs, and the more recent zk-Rollup technologies. We examine the unique properties and use cases of these techniques, highlighting their potential to enhance privacy, security, and trust in digital systems. Our research into zk-Rollups demonstrates significant improvements in transaction throughput and gas efficiency, paving the way for the extensive deployment of modern privacy-preserving solutions. Furthermore, we showcase several innovative applications of ZKPs, such as privacy-preserving decentralized finance (DeFi) protocols, anonymous credentials, and secure multi-party computation. These use cases illustrate the transformative potential of advanced ZKPs to improve privacy and security across a range of digital domains. To foster wider adoption of modern ZKPs, we have developed and opensourced a collection of advanced tools and libraries that simplify the implementation of ZKP-based solutions. We also offer practical guidance and best practices for developers and researchers working in this field, aiming to accelerate the development and real-world impact of modern zero-knowledge proof systems.
Heera Wali, Vishal Kulkarni, Rajashekar Ganiger, Nalini C. Iyer
This study investigates how blockchain technology can be used in the automotive industry and society, with a focus on addressing privacy concerns and compliance issues. The study investigates the application of privacy-preserving methods, including zero-knowledge proofs and anonymous credentials, to protect vehicle data shared with servers and prevent V2X channel spam. The proposed blockchain ecosystem involves collaborations among stakeholders like governmental bodies and automotive industry participants to ensure user privacy, implementation employs Ethereum[5], circom, and ezkl for practical realization, the study demonstrates the potential of blockchain and [7] Zero Knowledge Proofs in overcoming challenges, promoting a more secure digital environment.
The ongoing challenge in the world of blockchain technology is finding a solution to the trilemma that involves balancing decentralization, security, and scalability. This paper introduces a pioneering blockchain architecture designed to transcend this trilemma, uniting advanced cryptographic methods, inventive security protocols, and dynamic decentralization mechanisms. Employing established techniques such as elliptic curve cryptography, Schnorr verifiable random function, and zeroâknowledge proof (zkâSNARK), alongside groundbreaking methodologies for stake distribution, anomaly detection, and incentive alignment, our framework sets a new benchmark for secure, scalable, and decentralized blockchain ecosystems. The proposed system surpasses topâtier consensuses by attaining a throughput of 1700+ transactions per second, ensuring robust security against all wellâknown blockchain attacks without compromising scalability and demonstrating solid decentralization in benchmark analysis alongside 25 other blockchain systems, all achieved with an affordable hardware cost for validators and an average CPU usage of only 16.1%.
Radio Frequency Identification (RFID) promotes the fundamental tracking procedure of the Internet of Things (IoT) network due to its autonomous data collection as well as transfer incurring low costs. To overcome the insecure exchange of tracking data and to prevent unauthorized access, parallel dependency RFID grouping-proof protocol is applied by the reader to authenticate tags simultaneously. However, conventional grouping-proof authentication schemes are not sufficient for the memory constraint RFID tags due to the recurrent utilization of a 128-bit PRNG (Pseudo Random Number Generator) function. Alternatively, the existing parallel-dependency grouping-proof schemes are not able to overcome numerous limitations regarding session establishment, efficient key management, and multicast message communication within the specified group. In this research, a lightweight, secure, and efficient communication protocol is proposed to overcome the aforementioned limitations using Elliptic Curve Cryptography (ECC) and Zero-Knowledge property to establish a session key among the participated tags, reader, and remote server. The proposed scheme can work in offline mode. The proposed ECC-based parallel dependency grouping-proof scheme is referred to as ECC-PDGPP which abides by the rules of the EPC class-1 gen-2 (C1 G2) standard of RFID tags. Finally, the proposed protocol is analyzed using a formal random oracle model and simulated using a well-known AVISPA simulation tool that shows the proposed scheme is well protected against all potential security threats.