G. Sharmila, K. Neha, M. Kaviya, M. Juhe Sherin · 5 authors
Blockchain technology is a cutting-edge advancement in information technology. Bitcoin, as one of its initial uses, has attracted considerable attention as a cryptocurrency. Alongside Ethereum, which emphasizes blockchain-driven smart contracts, these technologies lie at the heart of modern cryptocurrency innovation. Off-chain transactions offer a scalable solution for blockchain networks, reducing congestion, lowering transaction fees, and improving processing efficiency without compromising decentralization. However, existing off-chain solutions often face security and flexibility challenges, particularly in environments with high latency and unstable connectivity. The proposed system leverages the Hardhat blockchain framework with Ethereum to enable secure peer-to-peer transactions from user wallets, ensuring seamless fund transfers even in offline conditions. Additionally, it integrates blockchain-based email functionality, allowing encrypted messages to be sent securely over a decentralized network, thereby enhancing data privacy and security. To further strengthen data integrity, the system incorporates the Inter Planetary File System (IPFS) for decentralized file storage, reducing reliance on centralized servers and minimizing data loss risks. By combining off-chain transactions, blockchain-based email, and IPFS storage, the system enhances efficiency, security, and reliability, offering a robust decentralized solution for financial transactions and secure communication. The data is distributed across all cryptocurrency users within the network. This ensures that when a user initiates a transaction, data mining processes are conducted.
Blockchain technology offers a powerful solution for securing and managing legal and academic documents by providing decentralization, transparency, and immutability. This research presents a blockchain-based framework for tamper-proof legal document and credential management aimed at improving the reliability and efficiency of traditional verification systems. In the proposed architecture, applicants submit their credentials, which are authenticated by issuing institutions and stored in the InterPlanetary File System (IPFS) for decentralized file management, while only cryptographic hashes are recorded on the blockchain to ensure integrity, reduce storage costs, and improve scalability. The system supports multiple consensus mechanisms, including Proof of Work, Proof of Stake, and Practical Byzantine Fault Tolerance, and experimental results demonstrate that Proof of Stake achieves the best balance between security and transaction efficiency. A prototype implementation achieved a transaction throughput of up to 1,000 transactions per second with an average confirmation time of 5 seconds, significantly reducing verification time and manual processing compared to conventional methods. Comparative analysis confirms that the proposed solution enhances security, minimizes fraud, and improves cost-effectiveness. The framework provides a strong foundation for future extensions such as cross-chain interoperability, AI-driven fraud detection, and mobile-based verification platforms
Blockchain technology introduces a new decentralized paradigm era avoiding the reliance on trusted third parties. It is a transparent and distributed ledger which is designed fundamentally for digital cryptocurrencies but has since been extended to various industries. However, its immutability obligates significant challenges including storing illicit contents, privacy regulations violations, and restricting data management flexibility. Therefore, redactable blockchain has emerged as a leading solution enabling controlled immutable contents amendment. Transaction-level redaction reinforced by fine-grained access control forms the cornerstone of the current redaction mechanisms. This redaction concept essentially depends on modifying mutable transactions governed by predefined access policies specified by the transaction owner. Modifiers equipped with necessary rewriting privileges and who fulfil the associated access policy are enabled to perform modifications. However, the existing redaction mechanisms infrastructures are inefficient. For instance, the chameleon hash ephemeral trapdoor (chet),
The rapid growth of digital technologies has intensified concerns about data privacy and security. Blockchain technology, combined with advanced cryptographic methods, presents a promising solution to enhance digital privacy by enabling decentralized, transparent, and tamper-resistant systems. This article explores the foundational principles of blockchain and cryptography, evaluates their interplay in preserving digital privacy, and examines current challenges and future directions. Through comprehensive analysis, the study highlights how cryptographic protocols such as zero-knowledge proofs and homomorphic encryption can significantly augment privacy on blockchain platforms, while addressing inherent scalability and usability challenges.
Although differential privacy (DP) is widely regarded as the de facto standard for data privacy, its implementation remains vulnerable to unfaithful execution by servers, particularly in distributed settings. In such cases, servers may sample noise from incorrect distributions or generate correlated noise while appearing to follow established protocols. This work addresses these malicious behaviours in a distributed client-server-verifier setup, under Verifiable Distributed Differential Privacy (VDDP), a novel framework for the verifiable execution of distributed DP mechanisms. We systematically capture end-to-end security and privacy guarantees against potentially colluding adversarial behaviours of clients, servers, and verifiers by characterizing the connections and distinctions between VDDP and zero-knowledge proofs (ZKPs). We develop three novel and efficient instantiations of VDDP: (1) the Verifiable Distributed Discrete Laplace Mechanism (VDDLM), which achieves up to a 400,000x improvement in proof generation efficiency with only 0.1--0.2x error compared with the previous state-of-the-art verifiable differentially private mechanism and includes a tight privacy analysis that accounts for all additional privacy losses due to numerical imprecisions, applicable to other secure computation protocols for DP mechanisms based on cryptography; (2) the Verifiable Distributed Discrete Gaussian Mechanism (VDDGM), an extension of VDDLM that incurs limited overhead in real-world applications; and (3) an improved solution to Verifiable Randomized Response (VRR) under local DP, as a special case of VDDP, achieving up to a 5,000x reduction in communication costs and verifier overhead.
Since Diffie and Hellman's pioneering work on asymmetric cryptography in 1976, digital signature technology has evolved through three phases—theoretical foundation, standardization, and diversified innovation—emerging as a cornerstone of trust in digital societies. Theoretically, foundational frameworks were established by RSA, DSA, and Schnorr algorithms. Standardization efforts, including NIST DSS, ISO/IEC series, and national systems (e.g., China's SM2/SM9, Russia's GOST), fostered a multipolar ecosystem. Extended-attribution technologies (blind, group, and ring signatures) addressed privacy and scenario-specific demands. Current challenges, such as quantum computing threats and privacy-regulation trade-offs, drive advancements in post-quantum cryptography (lattice-based signatures, hash-based XMSS) and privacy-enhancing mechanisms (verifiably encrypted signatures, homomorphic signatures), guided by ISO/IEC redactable standards and NIST's post-quantum initiative. Moving forward, digital signatures will deepen capabilities in provable security, quantum resistance, and adaptive policy control, underpinning trust architectures for emerging ecosystems like Web3 and the metaverse.
Open access
Cryptography and Data Security
Digital and Cyber Forensics
Advanced Steganography and Watermarking Techniques
This study introduces a cutting-edge architecture developed for the NewbornTime project, which uses advanced AI to analyze video data at birth and during newborn resuscitation, with the aim of improving newborn care. The proposed architecture addresses the crucial issues of patient consent, data security, and investing trust in healthcare by integrating Ethereum blockchain with cloud computing. Our blockchain-based consent application simplifies patient consent's secure and transparent management. We explain the smart contract mechanisms and privacy measures employed, ensuring data protection while permitting controlled data sharing among authorized parties. This work demonstrates the potential of combining blockchain and cloud technologies in healthcare, emphasizing their role in maintaining data integrity, with implications for computer science and healthcare innovation.
Don Roosan, Rubayat Khan, Saif Nirzhor, Fahmida Hai
Objective: The authors propose and evaluate a novel cybersecurity architecture for telehealth that is resilient against future quantum computing cyber threats. By integrating post-quantum cryptography (PQC) with quantum key distribution (QKD) and privacy-preserving mechanisms, data confidentiality and immutability for patient records in a post-quantum era are ensured. Methods: A multi-layered design approach was adopted. The PQC algorithms (e.g. CRYSTALS-Dilithium) were integrated at the blockchain consensus layer to resist quantum attacks. A directed acyclic graph (DAG)-based ledger managed high transaction throughput and latency constraints typical of telehealth. A QKD-enhanced key management protocol leveraged quantum channels for secure exchanges. Zero-knowledge proofs (ZKPs) and secure multiparty computation (MPC) verified transactions without exposing sensitive patient data. A granular access control model used attribute-based encryption and smart contracts to govern which participants could view or modify encrypted medical records. Results: The prototype was developed within a simulated telehealth network comprising hospitals, clinics, and patient devices. The PQC signatures at the consensus layer provided effective resistance to both classical and anticipated quantum attacks. The QKD facilitated secure key distribution, while ZKPs and MPC enabled validation of healthcare transactions without compromising patient privacy. Despite increased computational overhead, the DAG approach efficiently handled parallel transactions, indicating improved scalability compared to traditional linear blockchains. Conclusion: A QKD-enhanced, PQC-driven framework successfully addresses critical security and privacy requirements, safeguarding medical data from emerging quantum threats. Although overhead and infrastructural costs are significant, sustained cryptographic resilience and robust patient confidentiality underscore its suitability for next-generation healthcare systems. Future studies should explore additional optimizations, homomorphic encryption, and larger-scale pilots under regulatory standards.
Julio López Fenner, Carlos Castillo-Muñoz, Francisco Escobar, Ana Bustamante-Mora · 5 authors
Privacy-preserving secure multi-party computation protocols are known to face scalability and efficiency challenges in environments where participants hold distinct attributes of the same records (vertical partitioning) or controls a subset of complete records (horizontal partitioning), as in cross-institutional health data analysis or federated IoT analytics, mostly because of communication overhead and the need to address adaptability to large scale or heterogeneous settings. This work introduces a novel MPC protocol based on the Damgård–Jurik cryptosystem and Schnorr zero-knowledge proofs (ZKP), designed to securely aggregate private data distributed across a number of parties. By combining homomorphic encryption with non-interactive ZKP’s, the protocol ensures privacy, correctness, and scalability, aligning with the principles of privacy-enhancing technologies (PETs). Our approach minimizes data exposure, allowing participants to audit results, and achieves linear O(N) communication complexity, thus making it suitable for large-scale applications in secure data analytics and collaborative computing.
With the booming development of blockchain, it has gradually gained wide attention in the Internet of Things (IoT), finance, and other fields. However, due to the shared nature of blockchain ledgers among multiple users, sensitive user information, such as transaction amounts and private agreements, can be easily exposed. This poses significant privacy concerns for blockchain users. To address this issue, we propose TrustChain, a high-performance smart contract model based on the Trusted Execution Environment (TEE). TrustChain aims to safeguard the privacy of smart contract codes and user data by leveraging the secure execution environment provided by the TEE. Specifically, we introduce the TEE to run the smart contract with security and privacy without introducing a heavyweight cryptographic algorithm, thus improving the performance of the system. When running smart contracts, the operate nodes equipped with TEE ensure that the Operating System (OS) of the node itself cannot access the data within the TEE. This isolation effectively separates the sensitive information of the smart contract from the external environment. Furthermore, we introduce Verifiable Random Functions (VRFs) to randomly choose the operate nodes to prevent collusion attacks, further improving the security of the model. The graph ledger, based on the Directed Acyclic Graph (DAG), is used to adapt to the high-performance characteristics of a smart contract system based on the TEE. Finally, we simulate the scheme in TrustZone and demonstrate the feasibility of TrustChain through a series of experiments and analyses. The analysis and experimental results demonstrate that our solution exhibits excellent privacy protection performance and achieves higher throughput compared to traditional smart contracts. • We have introduced TrustChain, a smart contract model based on TEE, to ensure the privacy and security of smart contracts. • VRFs are proposed to randomly select operate nodes, preventing collusion and enhancing defense against malicious attacks. • The redesigned consensus mechanism limits blockchain storage to smart contract outputs, preventing leakage of sensitive information. • We enhanced smart contract performance by integrating a DAG-based ledger with TEE's low-latency execution.
Threshold multi-party fully homomorphic encryption (TMFHE) schemes enable efficient computation to be performed on sensitive data while maintaining privacy. These schemes allow a subset of parties to perform threshold decryption of evaluation results via a distributed protocol without the need for a trusted dealer, and provide a degree of fault tolerance against a set of corrupted parties. However, existing TMFHE schemes can only provide correctness and security against honest-but-curious parties. We construct a compact TMFHE scheme based on the Learning with Errors (LWE) problem. The scheme applies Shamir secret sharing and share resharing to support an arbitrary t-out-of-N threshold access structure, and enables non-interactive reconstruction of secret key shares using additive shares derived from the current set of online participants. Furthermore, the scheme implements commitment and non-interactive zero-knowledge (NIZK) proof techniques to verify the TMFHE operations. Finally, our experiments demonstrate that the proposed scheme achieves active security against malicious adversaries. It overcomes the limitation of existing TMFHE schemes that can only guarantee correct computation under passive semi-honest adversaries.
Fernando Castillo, Jonathan Heiss, Sebastian Werner, Stefan Tai
Blockchain and distributed ledger technologies (DLTs) facilitate decentralized computations across trust boundaries. However, ensuring complex computations with low gas fees and confidentiality remains challenging. Recent advances in Confidential Computing -- leveraging hardware-based Trusted Execution Environments (TEEs) -- and Proof-carrying Data -- employing cryptographic Zero-Knowledge Virtual Machines (zkVMs) -- hold promise for secure, privacy-preserving off-chain and layer-2 computations. On the other side, a homogeneous reliance on a single technology, such as TEEs or zkVMs, is impractical for decentralized environments with heterogeneous computational requirements. This paper introduces the Trusted Compute Unit (TCU), a unifying framework that enables composable and interoperable verifiable computations across heterogeneous technologies. Our approach allows decentralized applications (dApps) to flexibly offload complex computations to TCUs, obtaining proof of correctness. These proofs can be anchored on-chain for automated dApp interactions, while ensuring confidentiality of input data, and integrity of output data. We demonstrate how TCUs can support a prominent blockchain use case, such as federated learning. By enabling secure off-chain interactions without incurring on-chain confirmation delays or gas fees, TCUs significantly improve system performance and scalability. Experimental insights and performance evaluations confirm the feasibility and practicality of this unified approach, advancing the state of the art in verifiable off-chain services for the blockchain ecosystem.
Ruonan Chen, Ye Dong, Yizhong Liu, Tingyu Fan · 8 authors
Federated Learning (FL) is a distributed machine learning paradigm that allows multiple clients to train models collaboratively without sharing local data. Numerous works have explored security and privacy protection in FL, as well as its integration with blockchain technology. However, existing FL works still face critical issues. i) It is difficult to achieving poisoning robustness and data privacy while ensuring high model accuracy. Malicious clients can launch poisoning attacks that degrade the global model. Besides, aggregators can infer private data from the gradients, causing privacy leakages. Existing privacy-preserving poisoning defense FL solutions suffer from decreased model accuracy and high computational overhead. ii) Blockchain-assisted FL records iterative gradient updates on-chain to prevent model tampering, yet existing schemes are not compatible with practical blockchains and incur high costs for maintaining the gradients on-chain. Besides, incentives are overlooked, where unfair reward distribution hinders the sustainable development of the FL community. In this work, we propose FLock, a robust and privacy-preserving FL scheme based on practical blockchain state channels. First, we propose a lightweight secure Multi-party Computation (MPC)-friendly robust aggregation method through quantization, median, and Hamming distance, which could resist poisoning attacks against up to <50% malicious clients. Besides, we propose communication-efficient Shamir's secret sharing-based MPC protocols to protect data privacy with high model accuracy. Second, we utilize blockchain off-chain state channels to achieve immutable model records and incentive distribution. FLock achieves cost-effective compatibility with practical cryptocurrency platforms, e.g. Ethereum, along with fair incentives, by merging the secure aggregation into a multi-party state channel. In addition, a pipelined Byzantine Fault-Tolerant (BFT) consensus is integrated where each aggregator can reconstruct the final aggregated results. Lastly, we implement FLock and the evaluation results demonstrate that FLock enhances robustness and privacy, while maintaining efficiency and high model accuracy. Even with 25 aggregators and 100 clients, FLock can complete one secure aggregation for ResNet in 2 minutes over a WAN. FLock successfully implements secure aggregation with such a large number of aggregators, thereby enhancing the fault tolerance of the aggregation.
Stefanos Chaliasos, Imam Al-Fath, Alastair F. Donaldson
Zero-knowledge proofs (ZKPs) have evolved from a theoretical cryptographic concept into a powerful tool for implementing privacy-preserving and verifiable applications without requiring trust assumptions. Despite significant progress in the field, implementing and using ZKPs via \emph{ZKP circuits} remains challenging, leading to numerous bugs that affect ZKP circuits in practice, and \emph{fuzzing} remains largely unexplored as a method to detect bugs in ZKP circuits. We discuss the unique challenges of applying fuzzing to ZKP circuits, examine the oracle problem and its potential solutions, and propose techniques for input generation and test harness construction. We demonstrate that fuzzing can be effective in this domain by implementing a fuzzer for \texttt{zk-regex}, a cornerstone library in modern ZKP applications. In our case study, we discovered \textit{$10$} new bugs that have been confirmed by the developers.
While blockchain’s immutability ensures data integrity, it also poses significant challenges when dealing with illegal or erroneous data that require modification. The concept of redactable blockchain has emerged, utilizing Chameleon Hash (CH) and subsequent Policy-based Chameleon Hash (PCH) for controlled data editing. However, current redactable blockchain implementations exhibit significant limitations, particularly in their inability to separate data editing from policy modification and their insufficient support for decentralized management of diverse editing operations. To address these issues, this paper initially introduces the concept of Flexible Policy Chameleon Hash (FPCH), which integrates PCH with non-interactive zero-knowledge proofs to enable enhanced policy management flexibility. Moreover, this paper proposes a Redactable Blockchain Framework with Fine-grained Access Control (RBFAC) based on FPCH. The RBFAC framework employs a hybrid cryptographic approach to separate the right of data editing from policy modification. The framework also provides essential functionalities, including editing accountability, key tracking and revocation mechanisms, and policy privacy protection. Finally, experimental evaluations demonstrate that the RBFAC framework maintains acceptable performance overhead while delivering these advanced features. The results indicate that the proposed solution addresses the limitations of existing redactable blockchain systems, offering a more flexible and secure approach to controlled data editing in blockchain environments.
This research presents a novel framework and experimental results that combine zero-knowledge proofs (ZKPs) with private blockchain technology to safeguard whistleblower privacy while ensuring secure digital evidence submission and verification. For example, whistleblowers involved in corporate fraud cases can submit sensitive financial records anonymously while maintaining the credibility of the evidence. The proposed framework introduces several key innovations, including a private blockchain implementation utilising proof-of-work (PoW) consensus to ensure immutable storage and thorough scrutiny of submitted evidence, with mining difficulty dynamically aligned to the sensitivity of the data. It also features an adaptive difficulty mechanism that automatically adjusts computational requirements based on the sensitivity of the evidence, providing tailored protection levels. In addition, a unique two-phase validation process is incorporated, which generates a digital signature from the evidence alongside random challenges, significantly improving security and authenticity. The integration of ZKPs enables iterative hash-based verification between parties (Prover and Verifier) while maintaining the complete privacy of the source data. This research investigates the whistleblower’s niche in traditional digital evidence management systems (DEMSs), prioritising privacy without compromising evidence integrity. Experimental results demonstrate the framework’s effectiveness in preserving anonymity while assuring the authenticity of the evidence, making it useful for judicial systems and organisations handling sensitive disclosures. This paper signifies notable progress in secure whistleblowing systems, offering a way to juggle transparency with informant confidentiality.
In the context of cloud computing, services are held on cloud servers, where the clients send their data to the server and obtain the results returned by server. However, the computation, data and results are prone to tampering due to the vulnerabilities on the server side. Thus, verifying the integrity of computation is important in the client-server setting. The cryptographic method known as Zero-Knowledge Proof (ZKP) is renowned for facilitating private and verifiable computing. ZKP allows the client to validate that the results from the server are computed correctly without violating the privacy of the server’s intellectual property. Zero-Knowledge Succinct NonInteractive Argument of Knowledge (zkSNARKs), in particular, has been widely applied in various applications like blockchain and verifiable machine learning. Despite their popularity, existing zkSNARKs approaches remain highly computationally intensive. For instance, even basic operations like matrix multiplication require an extensive number of constraints, resulting in significant overhead. In addressing this challenge, we introduce $z k V C$, which optimizes the ZKP computation for matrix multiplication, enabling rapid proof generation on the server side and efficient verification on the client side. zkVC integrates optimized ZKP modules, such as Constraint-reduced Polynomial Circuit (CRPC) and Prefix-Sum Query (PSQ), collectively yielding a more than $\mathbf{1 2}$-fold increase in proof speed over prior methods. The code is available at https://github.com/UCF-Lou-Lab-PET/zkformer.
Blockchain-based digital voting systems have emerged as a promising solution to enhance the security, transparency, and accessibility of voting processes. By leveraging distributed ledger technology, these systems aim to mitigate various challenges associated with traditional voting methods, such as fraud, manipulation, and logistical complexities. However, the adoption of blockchain in voting introduces both security and usability considerations that must be carefully evaluated. In this paper, we conduct a comprehensive analysis of blockchain-based digital voting systems, focusing on their security and usability aspects. We examine the underlying cryptographic mechanisms, consensus protocols, and smart contract implementations to assess their resilience against potential attacks and vulnerabilities. Additionally, we investigate the user experience, accessibility, and scalability of these systems to evaluate their usability in real-world voting scenarios. Through this analysis, we aim to provide insights into the strengths, limitations, and trade-offs associated with blockchain-based digital voting systems, facilitating informed decision-making and further research in the field of secure and user-friendly electronic voting technologies.
This paper introduces the Cartesian Merkle Tree, a deterministic data structure that combines the properties of a Binary Search Tree, a Heap, and a Merkle tree. The Cartesian Merkle Tree supports insertions, updates, and removals of elements in $O(\log n)$ time, requires $n$ space, and enables membership and non-membership proofs via Merkle-based authentication paths. This structure is particularly suitable for zero-knowledge applications, blockchain systems, and other protocols that require efficient and verifiable data structures.
The rapid advancement of digital services and online interactions has highlighted the need for secure, user-centric identity management systems. Traditional identity solutions, often centralized and dependent on trusted third parties, pose challenges related to privacy, security, and control over personal data. Distributed Ledger Technology (DLT), particularly blockchain, offers a promising solution for decentralized identity management by enabling self-sovereign identities (SSI). Through the use of decentralized identifiers (DIDs) and verifiable credentials (VCs), DLT allows individuals to maintain full control over their personal information, eliminating the need for intermediaries while ensuring data integrity and privacy. This paper explores the key principles of DLT-based decentralized identity management, discussing its potential to enhance privacy, security, and interoperability in digital ecosystems. We examine the various technical frameworks, challenges, and standards in the field, with a focus on the integration of DLT with emerging technologies such as zero-knowledge proofs (ZKPs) and secure multiparty computation (SMPC). Additionally, we evaluate real-world use cases, from financial services to healthcare, and the role of regulatory frameworks in shaping the future of decentralized identity systems. Ultimately, DLT presents a paradigm shift in identity management, offering scalable, transparent, and trusted solutions for the digital age.
Damiano Di Francesco Maesa, Matteo Loporchio, Frank Tietze
This paper considers the application scenario of Intellectual Property (IP) management, a business process yet to fully embrace digitisation and the advantages it brings. We propose to leverage Distributed Ledger Technology (DLT) to digitise license agreements management by providing automated and trustworthy royalty computation, transaction execution, and payment distribution. This can be achieved by employing smart licenses, i.e., bundles of smart contracts implementing the royalty logic of license agreements. To provide scalability, flexibility, and resilience, we propose to deploy smart licenses on a network of networks model, i.e., a set of heterogeneous networks potentially running different DLT protocols and connected by cross-chain information exchange protocols. A novel advantage of the network of networks approach is that it allows for the use of private values for royalty computation, which is impossible in the traditional model. Of course, supporting private DLT networks requires privacy-preserving cross-chain schemes, a still open problem in the literature. This is why we present two alternative privacy-preserving cross-chain schemes for our considered application scenario of license agreements management, one based on Homomorphic Encryption (HE) and the other on Zero-Knowledge (ZK) proofs. Besides discussing their theoretical advantages and drawbacks, we present an experimental evaluation of a prototype implementation of smart licenses based on both schemes. • We propose a network of networks model that enhances scalability in smart license ecosystems. • The proposed model allows smart licenses to leverage data from heterogeneous networks, including private ones. • We compare two methods for privacy-preserving cross-chain communication: homomorphic encryption and zero-knowledge proofs. • We experimentally evaluate and compare two different privacy-preserving smart license implementations.
This paper introduces a certificate verification system powered by blockchain technology to prevent document forgery and ensure authenticity. By using a distributed ledger, the system creates a permanent and transparent record for issuing and verifying certificates. In this Block chain technology, block chain performs Secure Certificate Storage with hash encryption, real time data verification & decentralized network of nodes validation. By using block chain technology, we can prevents Forgery from tampering and ensure authenticity. It ensures the data integrity while doing real time verification and it is more efficient than other technology. It minimizes the verification cost. This application is more scalable and used in multiple areas like the educational sector for document verification and health industry to validate the medical records and Supply chain management. This project demonstrates the potential of blockchain technology in securing certificate verification, preventing forgery, and enhancing trust in document authenticity.
ABSTRACT Self‐sovereign identity management systems operate in open network environments and face security threats from semi‐trusted or malicious adversary models. In such environments, verifiable credentials are susceptible to attacks such as theft and forgery. In response to the privacy risks associated with verifiable credentials during issuance and revocation, this article proposes a privacy protection scheme for user information during the issuance and revocation processes of verifiable credentials in self‐sovereign identity management based on blockchain technology. First, a privacy‐preserving method that does not rely on a single identity provider and resists Sybil attacks has been designed using secure multi‐party computation cryptographic techniques. Second, the consortium blockchain committee nodes act as the issuer of verifiable credentials. By combining attribute commitments and zero‐knowledge proof techniques, the user's identity information is hidden, achieving the privacy protection goal during the issuance of verifiable credentials. Furthermore, in order to protect user privacy during the revocation of verifiable credentials (VCs), we employ a cryptographic accumulator technique to implement the revocation operation. This approach ensures the security of user privacy while effectively managing the revocation of credentials. Finally, this paper conducts a security analysis and performance evaluation of the proposed scheme. The results show that our scheme strikes a balance between security needs and time efficiency.
The integration of privacy-preserving transactions into public blockchains such as Ethereum remains a major challenge. The Stealth Address Protocol (SAP) provides recipient anonymity by generating unlinkable stealth addresses. Existing SAPs, such as the Dual-Key Stealth Address Protocol and the Curvy Protocol, have shown significant improvements in efficiency, but remain vulnerable to quantum attacks. Post-quantum SAPs based on lattice-based cryptography, such as the Module-LWE SAP, on the other hand, offer quantum resistance while achieving better performance. In this paper, we present a novel hybrid SAP that combines the Curvy protocol with the computational advantages of the Module-LWE technique while remaining Ethereum-friendly. In contrast to full post-quantum solutions, our approach does not provide quantum security, but achieves a significant speedup in scanning the ephemeral public key registry, about three times faster than the Curvy protocol. We present a detailed cryptographic construction of our protocol and compare its performance with existing solutions. Our results prove that this hybrid approach is the most efficient Ethereum-compatible SAP to date.