Lingyan Xue, Haiping Huang, Fu Xiao, Qi Li · 5 authors
Blockchain transaction privacy is a highly researched topic across various application scenarios. Current privacy-preserving schemes in blockchain employ advanced cryptographic techniques, such as homomorphic encryption and zero-knowledge proofs, to balance transaction privacy with regulatory requirements. However, these schemes encounter challenges, including computational inefficiency, data expansion, and overlooked metadata privacy, such as timestamp protection. In this paper, we first propose a privacy-enhanced traceable anonymous transaction scheme based on data transaction scenarios. This scheme integrates ring signature and Merkle hash tree techniques, effectively shortening the signature size and optimizing the verification process compared to existing combinations of ring signatures and zero-knowledge proofs. A novel verifiable timestamp privacy protection method is introduced, which obfuscates timestamps to prevent tampering without compromising integrity. To enhance scalability, this method extends to multiple transaction processing scenarios and implements a timestamp-sharing strategy to reduce the computational burden. It also allows tracking authorities to monitor the long-term addresses of both transaction parties if necessary. Rigorous security analysis and extensive experimental evaluations demonstrate that this scheme achieves superior privacy, traceability, and scalability compared to existing approaches.
The public ledger characteristic of blockchain grants data immutability but simultaneously introduces privacy leakage risks, making association analysis between on-chain behaviors and real-world identities possible. Existing privacy protection schemes struggle to balance the anonymity of the querier with the traceability of malicious behaviors. On one hand, legitimate inquiry behaviors are easily reverse-tracked by third parties through on-chain records (i.e., "human flesh search" targeting the querier); on the other hand, a completely anonymous environment may lead to data abuse without the possibility of accountability.To address this issue, this paper proposes an anti-"human flesh search" privacy protection system based on blockchain and zero-knowledge proofs. Addressing the aforementioned contradictions, this paper presents a blockchain data sharing scheme that balances privacy and regulation. The scheme utilizes IPFS to implement graded encrypted storage for large files. The core innovation lies in combining the Schnorr protocol and Chameleon Hash to construct a Blockchain Designated Verifier Proof (BDVP). While verifying user query permissions through blockchain smart contracts, the system utilizes the trapdoor property of the Chameleon Hash to achieve the non-transferability of proofs, preventing third parties from reverse-tracking the querier's identity by analyzing on-chain records<sup>[<xref ref-type="bibr" rid="R2">2</xref>]</sup>. Furthermore, the system introduces a threshold private key held by regulatory agencies to ensure that, in the event of data abuse, malicious users can be de-anonymized and held accountable according to the law.
Blockchain technology has garnered widespread attention across various sectors such as finance, healthcare, and government services due to its decentralized, tamper-resistant, and traceable nature. However, the conflict between privacy protection and regulatory compliance has emerged as a key barrier to its further development. Zero-Knowledge Proof (ZKP), as a next-generation privacy-enhancing technology, offers unique advantages in achieving "verifiability without disclosure." This paper systematically reviews the core technologies of blockchain and existing privacy protection methods, providing an in-depth comparison of mainstream ZKP protocols (e.g., zk-SNARK, zk-STARK, and PLONK) in terms of efficiency, security, and generality. It highlights how ZKP has become a technological pillar for privacy-compliant blockchain systems. The paper then explores practical application models of ZKP in cross-domain scenarios such as healthcare, finance, e-government, and supply chains, and further analyzes potential mechanisms through which ZKP can fulfill regulatory requirements such as AML, KYC, and auditing while preserving privacy. Finally, it discusses current technical bottlenecks and future development trends, including the integration of ZKP with AI, secure hardware, and post-quantum cryptography, as well as strategic frameworks for building a scalable and regulation-friendly blockchain ecosystem.
Abstract This paper thoroughly explores the complex interplay between blockchain technology and the General Data Protection Regulation (GDPR) of the European Union, alongside the substantial challenges and potential opportunities stemming from their interaction. While the challenges of decentralization and immutability in blockchain are well-documented, this paper advances the discussion by incorporating legal developments, such as evolving interpretations of joint controllership and new advisory opinions. It also evaluates emerging use cases, including blockchain integration in digital currencies like Worldcoin, highlighting contemporary compliance challenges and innovative solutions. By proposing actionable frameworks that leverage technological advancements like chameleon hashes and zero-knowledge proofs, this paper provides a forward-looking analysis of how blockchain systems can align with GDPR principles, offering theoretical insights and practical pathways for compliance. The conclusion underscores the urgent need for clear regulatory frameworks. These frameworks are crucial to enable a balanced approach that fosters innovation while ensuring robust data protection compliance, and their absence could hinder the potential impact of the research.
Traditional identity authentication algorithms that rely on centralized trust authorities and plaintext identity verification often suffer from privacy leakage, key misuse, and single-point-of-failure risks. This study proposes a lightweight, privacy-preserving authentication algorithm based on elliptic curve and zero-knowledge proofs to address these issues. The proposed scheme introduces a random challenge and an anonymous verification mechanism during the authentication process to ensure both identity privacy and authentication security. While maintaining high levels of security and verifiability, the algorithm effectively reduces computational complexity and communication overhead. Experimental results demonstrate that the proposed method significantly outperforms traditional RSA and ECDSA in terms of authentication delay, communication cost, and security robustness. This approach is practical and scalable, offering a promising solution for secure authentication in environments with limited resource.
As network applications rapidly evolve toward mobile and ubiquitous scenarios, identity authentication protocols face heightened demands for privacy protection and computational efficiency while maintaining security.Traditional authentication schemes often struggle to achieve an effective balance between privacy preservation, computational complexity, and security during design, with performance bottlenecks becoming increasingly prominent in resource-constrained environments.To address these challenges, this study proposes an optimized algebraic curve identity authentication protocol incorporating zero-knowledge proofs.Building upon Elliptic Curve Cryptography (ECC) as its cryptographic foundation, the protocol leverages ECC's inherent advantages of shorter key lengths and higher computational efficiency for equivalent security levels.Simultaneously, it integrates zero-knowledge proof mechanisms to minimize the exposure of user identity information during authentication.Through systematic optimization of the key generation mechanism, zero-knowledge proof interaction flow, and identity verification logic, the proposed protocol effectively reduces computational and communication overhead while ensuring identity anonymity and authentication integrity.Experimental results demonstrate that compared to traditional ECC authentication protocols and classical zero-knowledge proof schemes, the optimized protocol exhibits significant advantages in key generation time, authentication response latency, and communication load.It effectively resists common security threats such as replay attacks and forgery attacks, making it suitable for resource-constrained network environments and privacy-sensitive applications.
Zero-knowledge proofs provide cryptographic guarantees of statement validity without revealing underlying secrets. However, static proofs enable linking attacks where adversaries track the same proof across multiple uses, compromising user privacy. We introduce Time-Based Re-randomization (TBR), a novel protocol that automatically transforms zero-knowledge proofs at fixed time intervals while preserving their validity. Our construction leverages cryptographic randomization combined with deterministic time-slot generation to create temporally unlinkable proofs without user interaction. We provide formal security proofs demonstrating that TBR maintains zero-knowledge and soundness properties while preventing proof-linking attacks. Performance analysis shows TBR incurs only 8-12ms overhead compared to 450-600ms for generating fresh proofs, making it practical for privacy-preserving applications including anonymous authentication, timelimited credentials, and blockchain systems.
The rapid expansion of the digital economy heightens the need for privacy and trust in intellectual property transactions. Traditional centralised approaches to identifying legal conflicts in intellectual property contracts are prone to data leakage and fail to balance transparency with confidentiality. This paper proposes a self-identification method for legal conflicts in intellectual property contracts using zero-knowledge proofs. By combining a light gradient boosting machine learning model with the zero-knowledge succinct non-interactive argument of knowledge protocol, our approach allows verifiable detection of potential legal conflicts without revealing sensitive information. Experiments on the US patent and trademark office patent dataset demonstrate that the method achieves high performance in conflict prediction (area under the receiver operating characteristic curve = 0.872) and verification efficiency (<10 ms), providing a novel and practical framework for privacy-aware legal technology.
The growth in the technology of cloud storage has meant that more and more organizations outsource large amounts of data to the cloud for storage, for archival and compliance purposes. This offers cost and scalability benefits but it introduces issues around verifying data integrity and the risk of leaking sensitive during audits by third parties. Past public auditing frameworks rely on interactive challenge-response mechanisms that leak metadata, increase audit latency and are susceptible to man in the middle attacks. This research proposes a non-interactive, privacy preserving public auding framework using Zero-knowledge Scalable Transparent Arguments of Knowledge (zk-STARKS). Using the Winterfell library to generate cryptographic proofs that a data set remains unchanged without revealing the content of the data or structural metadata to the verifier. The frame is deployed using a cloud native architecture, composed of AWS services. Evaluation of a 200MB dataset split into 2mb per block achieved proof and verification times of approx. 900 ms per block, which can be scaled up to larger datasets with limited cloud compute costs. The result confirm that zk-STARKS can be used to implement an efficient, trust less and privacy preserving framework for cold data auditing, detecting tampering of data without the need for a trusted TPA.
Wenjie Qu, Yijun Sun, Xuanming Liu, Tao LU · 7 authors
Large Language Models (LLMs) are widely employed for their ability to generate human-like text. However, service providers may deploy smaller models to reduce costs, potentially deceiving users. Zero-Knowledge Proofs (ZKPs) offer a solution by allowing providers to prove LLM inference without compromising the privacy of model parameters. Existing solutions either do not support LLM architectures or suffer from significant inefficiency and tremendous overhead. To address this issue, this paper introduces several new techniques. We propose new methods to efficiently prove linear and nonlinear layers in LLMs, reducing computation overhead by orders of magnitude. To further enhance efficiency, we propose constraint fusion to reduce the overhead of proving non-linear layers and circuit squeeze to improve parallelism. We implement our efficient protocol, specifically tailored for popular LLM architectures like GPT-2, and deploy optimizations to enhance performance. Experiments show that our scheme can prove GPT-2 inference in less than 25 seconds. Compared with state-of-the-art systems such as Hao et al. (USENIX Security’24) and ZKML (Eurosys’24), our work achieves nearly 279× and 185× speedup, respectively.
Digital credential wallets manage identity documents such as government IDs and financial certificates, face the trilemma of privacy, security, and usability. Optimizing for anonymity by using Anonymous Credentials enhances privacy, but introduces challenges. Current benchmarks show verification using zero-knowledge proofs of knowledge taking 50–500ms, far exceeding the <1ms of standard credentials, impeding usability. Additionally, anonymity complicates security: preventing multiple-credential issuance (sybil resistance) or enforcing revocation becomes difficult when both users and objects are essentially secret. These issues are urgent due to the EU’s 2026 mandate for EU-wide credential wallet usage, which will drive widespread adoption of digital credential wallets, while critical use cases, like privately combining credentials from multiple issuers for KYC, emphasize the importance of this work. This thesis extends existing work and develops new, fast cryptographic primitives for privacy preserving credential wallets. It introduces the fastest anonymous credential scheme with a 3.77ms Show+Verify time for 10 attributes, outperforming prior methods by 10-15%. Three extensions enhance this scheme. 1) formalized Identity Binding property for secure multi-issuer, multi-credential verification, with an implementation verifying 16 credentials from unique issuers in 72ms; 2) new nullifier constructions using Σ-protocols without pairings, improving privacy-preserving sybil resistance by 5x over previous approaches; 3) T-SIRIS, a threshold-issued, sybil-resistant identity system with near-constant Show+Verify times, over 30x faster than comparable systems [RAR+24]. These advancements are validated by an open-source Rust benchmarking library, delivering standardized empirical data across anonymous credential schemes.