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.
With the rapid development of the digital economy and the Internet of Things, identity authentication in resource-constrained environments faces challenges such as low efficiency and inadequate privacy protection. Addressing the high computational and communication overhead of traditional RSA and ECC authentication mechanisms, this study proposes an efficient identity authentication mechanism (AC-ZKP) based on algebraic curves and non-interactive zero-knowledge proofs (NIZK). This mechanism leverages algebraic curve group operations to achieve lightweight key management and employs zero-knowledge proofs to ensure information concealment and anti-forgery during identity verification. The paper conducts a systematic study across four dimensions: system modeling, algorithm design, security analysis, and performance evaluation. Experimental results demonstrate that while maintaining 128-bit security strength, the AC-ZKP mechanism reduces authentication latency by approximately 44% and communication overhead by about 40%. It also exhibits strong scalability and resistance to attacks, significantly outperforming traditional ECC schemes. These findings provide a viable solution for lightweight, high-security identity authentication in IoT, edge computing, and cross-border data exchange environments.
In the digital age, information security is crucial. Zero knowledge proof, as an innovative cryptographic technique, can effectively prove the authenticity of information without revealing the information itself. This article delves into the application of zero knowledge proof methods in information security systems, analyzing their principles, advantages, challenges, and future development trends, providing theoretical support and practical references for research and practice in the field of information security.
Proof-of-Context (PoC) protocols aim to ensure fairness and integrity in smart contract execution by cryptographically binding on-chain transactions to verifiable off-chain contextual data. Traditional consensus mechanisms (e.g., Proof-of-Work, Proof-of-Stake) focus on ordering and validation of transactions but do not address whether the contextual conditions that should govern contract execution are satisfied. In this manuscript, we propose a novel PoC framework that leverages decentralized oracles, zero-knowledge proofs, and time-stamped Merkle commitments to provide verifiable evidence that all pre-specified preconditions and environmental parameters were met at execution time. We detail the design of the protocol, implement a prototype on an Ethereum testnet using Chainlink oracles and zk-SNARKs, and conduct a performance evaluation under varying network and workload conditions. Our results show that PoC incurs a modest overhead—on average 5% additional gas cost and 200 ms added latency per proof generation—while dramatically enhancing auditability and reducing the risk of context-based manipulation or dispute. We conclude that PoC protocols offer a practical mechanism for enforcing fairness in a wide range of decentralized applications, from DeFi loans conditioned on real-world data to NFT minting events gated by dynamic criteria. Finally, we discuss the scope, limitations, and future research directions for broader deployment.
S.E. Bootsma, F. Ter Ellen, D.P. Otto, T.J. Sijpesteijn
Can you prove something without revealing the data behind it? Your personal data, your contacts, your preferences and your (online) behaviour are incredibly valuable. With many services tracking our every move, maintaining privacy can feel like an impossible challenge. One might argue, “Just avoid services that collect personal data.” If you disagree with how a given platform handles your information, you could choose not to sign up. But in practice, the decision is rarely that straightforward. In many cases, individuals lack meaningful alternatives or face exclusion from essential services.
N Sherrin Sophia, Prisha Gupta, M Sharma, Mohona Ghosh · 5 authors
The transformation of fundraising through the use of crowdfunding platforms has come with obstacles such as fraud, verification and misuse of funds, and lack of transparency. The use of smart contracts in conjunction with Zero-Knowledge Proofs (ZKPs) of er advanced privacy and security through the validation of identity concealment during participant contributions. This privacy enhances trustworthiness and motivates user engagement. The model provides adequate allocation of funds which improves overall transparency. The solution provides a better distribution of challenges that are posed in mainstream models of crowdfunding by integrating enhanced security, eficiency, and accountability while performing fundraising through the examined approach.
Zero-knowledge (ZK) proofs have been increasingly popular in privacy-preserving applications and blockchain systems.To facilitate handy and efficient ZK proof generation for normal users, the industry has designed domain-specific languages (DSLs) and ZK compilers.Given a program in ZK DSL, a ZK compiler compiles it into a circuit, which is then passed to the prover and verifier for ZK checking.However, the correctness of ZK compilers is not well studied, and recent works have shown that de facto ZK compilers are buggy, which can allow malicious users to generate invalid proofs that are accepted by the verifier, causing security breaches and financial losses in cryptocurrency.In this paper, we propose MTZK, a metamorphic testing framework to test ZK compilers and uncover incorrect compilations.Our approach leverages deliberately designed metamorphic relations (MRs) to mutate ZK compiler inputs.This way, ZK compilers can be automatically tested for compilation correctness using inputs and mutated variants without requiring manual intervention.We propose a set of design considerations and optimizations to deliver an efficient and effective testing framework.In the evaluation of four industrial ZK compilers, we successfully uncovered 21 bugs, out of which the developers have promptly patched 15.We also show possible exploitations of the uncovered bugs to demonstrate their severe security implications.
Introduction:The study examined blockchain technology as a pillar of Web3, highlighting its principles of immutability, transparency, and decentralization. It analyzed the paradox that these same virtues could become disadvantages when it was necessary to correct errors, delete data, or deal with malicious uses, generating legal and ethical tensions.Development:Cases and studies were reviewed that showed how immutability guaranteed integrity and resistance to censorship but was incompatible with rights such as the “right to be forgotten” under the GDPR. Situations were also documented in which decentralization empowered both legitimate actors and criminals, eliminating consumer protection mechanisms. Faced with these dilemmas, solutions such as off-chain storage, updatable smart contracts, decentralized identity, and zero-knowledge proofs were evaluated. The proposal for double validation was highlighted, which incorporated a layer of smart contract verification to authenticate the origin and legitimacy of information before it was recorded. The validation of sensitive content by the people involved was also proposed as a strategy to prevent defamation, misinformation, or the dissemination of illegal material.Conclusion:The paper concluded that the potential of blockchain lay in its integration within an ethical, legal, and social framework. The implementation of mandatory verification and validation mechanisms strengthened accountability and individual protection, transforming blockchain into a tool that is not only secure and transparent, but also fair and socially responsible.
Abstract The aim of this study is to design and implement a system that allows centralized blockchain institutions to prove their solvency. This system ensures that institutions do not misappropriate user assets and enhances trust between users and institutions. The article introduces the Groth‐16 zero‐knowledge proof algorithm from ZK‐SNARK (zero‐knowledge succinct non‐interactive argument of knowledge). The R1CS arithmetic circuit in the Groth‐16 algorithm effectively guarantees the authenticity and tamper‐resistance of the system's raw data sources. Additionally, it combines the use of Merkle Sum Trees and Sparse Merkle trees. The former enables users to perform distributed verification of solvency proofs, while the latter effectively hides the overall number of users. Finally, users verify the balances and the private key signatures of addresses in the institution's bulletin board. Together, these components form a comprehensive and distributed solvency proof solution. This solution is a pioneering solution in the field of blockchain solvency proofs and provides a secure, efficient, and privacy‐preserving method for centralized cryptocurrency service providers or Web3 enterprise custodians. It effectively addresses the challenge of proving an institution's possession of sufficient reserves to cover user assets without compromising user privacy or disclosing the institution's scale.
As generative AI (GenAI) technologies proliferate in urban governance, the challenge of building trustworthy AI systems becomes increasingly urgent. This chapter critically examines “trustworthiness” not as a purely technical attribute, but as a socio-political construct shaped by power, participation, and policy. Focusing on smart cities as testbeds of algorithmic governance, it explores how decentralized Web3 technologies—such as blockchain, DAOs, and data cooperatives—can offer structural alternatives to centralized, opaque systems. Drawing on action research from the Horizon Europe ENFIELD project and framed by EU policy developments like the AI Act and the Draghi Report, the chapter proposes a multi-layered governance model. It evaluates seven emerging techniques to strengthen GenAI accountability: (i) federated learning, (ii) blockchain provenance tracking, (iii) zero-knowledge proofs, (iv) DAO-based verification, (v) digital watermarking, (vi) explainable AI (XAI), and (vii) privacy-preserving machine learning (PPML). The chapter ultimately argues that trustworthy AI must be embedded in participatory governance, algorithmic transparency, and plural civic oversight. By reframing trust as a relational, institutional, and democratic issue, it contributes to reimagining smart cities not as technocratic projects, but as inclusive arenas for data justice and democratic renewal.
Ensuring the reproducibility of scientific simulations is a persistent challenge, despite current best practices like version control and containerization. Factors such as floating-point arithmetic variations, hardware differences, and concurrency issues often prevent bit-for-bit replication of results. This paper investigates the techniques that distributed ledger technologies employ to achieve deterministic computations and application of these techniques to enhance the reproducibility, trustworthiness and verifiability of scientific simulations. We explore two primary approaches: executing simulations directly “on-chain” for complete transparency and deterministic replay, and performing computations “off-chain” while anchoring their integrity to a blockchain via cryptographic proofs, such as Zero-Knowledge Proofs (ZKPs) and Merkle trees.
Zero-knowledge proofs allow verification of computations without revealing private information. However, existing systems require memory proportional to the computation size, which has historically limited use in large-scale applications and on mobile and edge devices. We solve this fundamental bottleneck by developing, to our knowledge, the first proof system with sublinear memory requirements for mainstream cryptographic constructions. Our approach processes computations in blocks using a space-efficient tree algorithm, reducing memory from linear scaling to square-root scaling--from $Θ(T)$ to $O(\sqrt{T} + \log T \log\log T)$ for computation size $T$--while maintaining the same proof generation time through a constant number of streaming passes. For widely-used linear polynomial commitment schemes (KZG/IPA), our method produces identical proofs and verification when using the same parameters and hashing only aggregate commitments into the challenge generation, preserving proof size and security. Hash-based systems also achieve square-root memory scaling though with slightly different proof structures. This advance enables zero-knowledge proofs on everyday devices and makes previously infeasible large computations verifiable, fundamentally democratizing access to privacy-preserving computation. Space-efficient zero knowledge proof systems create opportunities to reshape how trust is established in digital systems--from enabling widespread participation in decentralized networks to making verifiable scientific computing practical at unprecedented scales.