Threshold signatures, notably ECDSA, are fundamental for securing decentralized applications.Their non-linear structure poses challenges in distributed signing, often tackled by pairwise multiplicative-to-additive share conversion, leading to O(n) communication and O(n 2 ) verification costs for each of n signers.Moreover, most schemes lack robustness, necessitating a complete restart upon fault.A pioneering work by Wong et al. (NDSS '23) still requires rolling back to the preceding round to resume signing after another round to convince all other signers.We revisit secure multiparty computation from threshold linearly homomorphic encryption (LHE).Realizing its public verifiability and fault recovery, we encompass two technical contributions to Castagnos-Laguillaumie LHE (CT-RSA '15): a 2-round robust distributed key generation (DKG) protocol in the dishonest majority setting and an accompanying zero-knowledge proof allowing extraction in an unknown-order group.We extend the DKG with dual-code-based verification (ACNS '17), upgrading its O(tn 2 )-cost private verifiability to an O(n 2 ) public one.Built on our DKG, we present the first threshold ECDSA protocol with O(1) communication and O(n) verification perparty costs while matching the lowest round complexity of nonrobust schemes (CCS '20).Empirically, we halve the computation and communication costs of the signing phase compared to stateof-the-art robust threshold ECDSA (NDSS '23).We also illustrate the versatility of our techniques with an improved threshold extension (IEEE S&P '23) of BBS+ signatures (IEEE Syst.J. '13).
Developing a fair, efficient, and scalable data trading protocol in decentralized networks has attracted much research effort recently. Zero-knowledge contingent payments (ZKCP) allows sellers and buyers to complete their trade fairly over the blockchain using zero-knowledge proofs. However, it suffers from memory-intensive requirements and scalability limitations. In this paper, we propose a practical data trading protocol tailored for Sudoku solutions, which is fair, efficient, and scalable. The core component of our protocol is a zero-knowledge argument for the correctness of a Sudoku solution of homomorphic encryption. This argument achieves sublinear communication complexity and the number of group exponentiations for both proving and verification is linear in the size of Sudoku solutions. The security of our protocol can be proven in the random oracle model under the Decision Diffie-Hellman assumption. In addition, we devise a mechanism that allows buyers to recover the private key through two zero-knowledge proofs and prevents the direct exposure of the decryption key. Furthermore, we implement the proposed protocol on the Ethereum testnet, and the experimental results show a significant improvement in overall efficiency.
Lennart Braun, Guilhem Castagnos, Ivan Damgård, Fabien Laguillaumie · 7 authors
We present distributed key generation and decryption protocols for an additively homomorphic cryptosystem based on class groups, improving on a similar system proposed by Braun, Damgård, and Orlandi at CRYPTO ‘23. Our key generation is similarly constant round but achieves lower communication complexity than the previous work. This improvement is in part the result of relaxing the reconstruction property required of the underlying integer verifiable secret sharing scheme. This eliminates the reliance on potentially costly proofs of knowledge in unknown order groups. We present a new method to batch zero-knowledge proofs in unknown order groups which strengthens these improvements. We also present a protocol which is proven secure against adaptive adversaries in the single inconsistent player (SIP) model. Our protocols are secure in the universal composability (UC) framework and provide guaranteed output delivery. We demonstrate the relative efficiency of our techniques by presenting the running times and communication costs associated with our implementation of the statically secure protocol and provide a direct comparison with alternate state of the art constructions.
Thế Dũng Bùi, Geoffroy Couteau, Pierre Meyer, Alain Passelègue · 5 authors
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 party’s public key. In addition to these theoretical contributions, we detail various optimizations and provide concrete instantiations of our paradigm relying on the Boneh-Ishai-Passelègue-Sahai-Wu wPRF and the Goldreich-Applebaum-Raykov wPRF. Putting everything together, we obtain public-key PCFs with a throughput of 15k–40k 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 (ε, δ)-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 (ε = 0.55, δ = 10−5)-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 UAVs’ 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 chains’ 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-Rodríguez, Salvador Sánchez‐Alonso, Miguel‐Ángel 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.
Electronic healthcare (E-health) cloud system enables electronic health records (EHRs) sharing and improves efficiency of diagnosis and treatment. In order to address EHRs confidentiality and authorized user access control in E-health cloud, attribute-based proxy re-encryption (ABPRE) has been widely employed which provides dynamic fine-grained access control over encrypted EHRs. Unfortunately, existing ABPRE schemes still have the following defects: 1) capacity of attribute-universe is defined at setup; 2) verifiable mechanism for re-encryption reveals EHRs about patients; 3) traditional access policy reveals sensitive information pertaining to patients. This paper focuses on these issues and presents large-universe, verifiable and privacy-preserving dynamic fine-grained access control scheme for E-health cloud. More details, we solve limitation of attribute-universe to large-universe, which means that attributes aren’t required to be enumerated at setup. Considering disclosure of underlying EHRs in verifiable mechanism, scheme introduces non-interactive zero-knowledge proof as verifiable mechanism that supports public validation and doesn’t leak EHRs of patients. Furthermore, partially hidden policy is employed to protect privacy of patients in policy, which divides attribute into attribute name and attribute value, displaying attribute name and hiding attribute value. Finally, experimental evaluation is given that demonstrates the more comprehensive functionality of our scheme without sacrificing significant computational overhead.
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 Lúcia, Amr Youssef, Giuseppe Franzè
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 control’s 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 plant’s actuator to validate the computations accomplished by the encrypted cloud-based networked controller without compromising the control scheme’s 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.