Stablecoins face an unresolved trilemma of balancing decentralization, stability, and regulatory compliance. We present a hybrid stabilization protocol that combines crypto-collateralized reserves, algorithmic futures contracts, and cross-chain liquidity pools to achieve robust price adherence while preserving user privacy. At its core, the protocol introduces stabilization futures contracts (SFCs), non-collateralized derivatives that programmatically incentivize third-party arbitrageurs to counteract price deviations via adaptor signature atomic swaps. Autonomous AI agents optimize delta hedging across decentralized exchanges (DEXs), while zkSNARKs prove compliance with anti-money laundering (AML) regulations without exposing identities or transaction details. Our cryptographic design reduces cross-chain liquidity concentration (Herfindahl-Hirschman Index: 2,400 vs. 4,900 in single-chain systems) and ensures atomicity under standard cryptographic assumptions. The protocol's layered architecture encompassing incentive-compatible SFCs, AI-driven market making, and zero-knowledge regulatory proofs. It provides a blueprint for next-generation decentralized financial infrastructure.
Efficient and secure sharing of scientific data remains a key challenge in the Open Science framework, especially in terms of data authenticity, provenance and privacy. Traditional digital repositories improve access but often lack decentralized mechanisms that guarantee integrity and traceability. Blockchain technology provides a potential solution through tamper-proof records and distributed consensus, while Zero Knowledge Proofs (ZKP) can enhance privacy protection. This study explores how blockchain and ZKP can be integrated for decentralized scientific data management. A systematic literature review reveals limited application of these combined technologies in Open Science, highlighting a research gap and the need for solutions that support transparent, secure and privacy-preserving data sharing in accordance with FAIR principles.
Ahmed Sayed M. Metwally, Yazeed Alhumaidan, Saad Alzahrani, Mohamed H. Abdelati
Implementing artificial intelligence (AI) and blockchain technology in management systems transforms traditional libraries into advanced information centers that are data-driven and effectively managed. While these technologies enhance efficiency and operational capabilities, they also present two critical challenges: data privacy and ethical concerns. This study examines the role of AI and blockchain in library management, focusing on issues related to data privacy and ethical challenges that arise from their use. It also offers best practices to ensure safe implementation. The research adopts a comprehensive mixed-methods approach, involving qualitative interviews and quantitative surveys to identify these challenges within the system architecture, assess the effectiveness of current designs, and propose a complete framework using privacy-preserving technologies. This framework incorporates innovative cryptographic techniques, including homomorphic encryption, differential privacy, and zero-knowledge proofs, providing a novel model for the ethical use of AI in libraries. The findings indicate that robust data protection, transparency, and accountability are essential to building trust in AI-powered library services.
In this work, we present a concretely efficient lattice-based zero-knowledge proof system that satisfies straight-line extractability. At the heart of our construction is a novel, non-generic application of the Katsumata transform (CRYPTO 2021) to the widely adopted zero-knowledge framework of Lyubashevsky, Nguyen and Plançon (CRYPTO 2022). Our approach significantly optimizes the proof size, achieving a 2X reduction compared to prior generic techniques. This makes our scheme particularly well-suited for anonymous credential systems, secure multi-party computation, and proving quantum security, where straight-line extractability is essential.
A zero-knowledge proof (ZKP) is an interactive proof between a prover and a verifier, with the prover's goal to convince the verifier that a solution of a computation problem exists without revealing any information about the solution itself. A card-based physical ZKP is an implementation of a ZKP using physical cards. Several metrics are used to measure the efficiency of card-based ZKPs, such as the numbers of required cards and shuffles. In this paper, we propose the first card-based ZKP for a puzzle Sudoku that uses only one shuffle. To achieve this, we use a technique of applying private operations interactively to prepare inputs for the protocol.
As Internet technology develops swiftly, the significance of privacy protection is escalating in the realms of e-commerce, e-government and software security. Due to the combination of the benefits of proxy signatures and blind signatures, the proxy blind signature scheme not only distributes the workload across application networks but also effectively safeguards the confidentiality of sensitive information. Additionally, the identity-based proxy blind signature protocol can avoid the problem of heavy certificate management and is widely used in electronic commerce and other scenarios. However, some identity-based proxy blind signature protocols that rely on the large integer factorization problem and the discrete logarithm problem are unable to withstand from attacks from quantum computers. Furthermore, current lattice-based proxy blind signature protocols offer only heuristic security and require three rounds of information exchange during the signing phase. In this paper, we introduce a new two-round identity-based proxy blind signature scheme based on lattices. This scheme utilizes a zero-knowledge proof protocol on lattices as its core component to develop an interactive two-round signature scheme that is free from security proof vulnerabilities. Additionally, we validate the security of the proposed protocol within the random oracle model and conduct a performance analysis.
The Blockchain-Based Decentralized Healthcare Data Management System with Ring Signatures and Zero- Knowledge Proofs (ZKPs) offers a state-of-the-art method for securely storing Electronic Health Records (EHRs) while ensuring impenetrable protection and confidentiality. This solution integrates blockchain technology with ZKPs and Ring Signatures to address these challenges, enabling secure and private information management. By leveraging ZKPs, the system ensures privacy during authentication by allowing healthcare providers to demonstrate their authority to access specific patient records without disclosing sensitive information. Ring Signatures enable any authorized entity within a defined group to modify data through anonymous yet authenticated transactional signing, preserving identity confidentiality. The primary goal of this approach is to establish a decentralized framework that ensures data accuracy, confidentiality, and accountability, granting patients secure and verified ownership of their medical records. Preliminary findings highlight the system's effectiveness in safeguarding patient privacy, facilitating transparent communication between patients and providers, and preventing unauthorized modifications. The study also evaluates accessibility and sustainability across medical networks, yielding promising results for real-world healthcare applications. This blockchain-based approach lays the foundation for a safer, more patient-centered, and collaborative healthcare information ecosystem.
As cyber-crimes have become more complex network forensics has become an essential element of cybersecurity investigations. However, conventional forensic techniques are confronted with challenges such as data privacy, integrity, and secure authentication of evidence. This paper suggests a privacy-preserving AI-augmented forensic framework that uses Zero-Knowledge Proofs (ZKP) for authenticating forensics securely and blockchain for tamper-evident forensic storage. The intended framework employs AI and ML strategies for real-time intrusion detection real-time intrusion detection, anomaly recognition, and cyber-attack attribution, radically enhancing forensic efficacy and investigative productivity. Experimental evidence obtained with the UNSW-NB15 dataset provides evidence that the AI model offers a detection rate of 97.5% accompanied by precision as high as 96.8% and a recall of as much as 98.2% to ensure good cyber threat classification. Moreover, the verification process of ZKP takes only 1.2 milliseconds, allowing for fast forensic validation with data confidentiality being maintained. The blockchain-based logging system for forensics has an overhead of merely 0.35 MB per transaction, allowing tamper-proof and scalable storage of forensic data. The findings confirm that integrating AI, ZKP, and blockchain improves forensic trustworthiness at the cost of reduced data exposure. This work adds to developing privacy-protecting forensic techniques and offers a secure, scalable solution for contemporary cybercrime investigations.
Karthikeyan Marappan, C L Annapoorani, Nazura Javed, Udhaya Sankar T P · 6 authors
Enhancing digital security has become essential in the age of decentralized systems. Zero-Knowledge Proof (ZKP) is a cryptographic method that allows one party to demonstrate ownership of certain knowledge without disclosing the actual data. When used with blockchain, Zero-Knowledge Proofs significantly improve privacy, validation, and data secrecy. This study investigates the use of Zero-Knowledge Proof in blockchain environments to enhance trustless authentication, secure transactions, and identity safeguarding. The main aim is to provide safe conversations while preserving transparency and anonymity. The immutable characteristics of blockchain, together with the privacy-preserving protocols of zero-knowledge proofs, allow applications like secret smart contracts, decentralized identification systems, and safe voting methods. The collaboration between blockchain and zero-knowledge proofs mitigates data exposure concerns and diminishes dependence on conventional centralized verification. This strategy aids in the development of robust digital infrastructures that can withstand cyber-attacks and data breaches. The incorporation of Zero-Knowledge Proof in blockchain signifies a progressive improvement in the protection of sensitive digital transactions across several industries.
The existing methods do not effectively meet the security and performance demands for Internet of Vehicles (IoV) applications. They also do not provide low-latency, secure edge-computing solutions for end-users in vehicular environments. The study presented in this paper proposes a blockchain-based edge computing framework that utilises Double Deep Q-Network (DDQN) for reinforcement learning and lightweight Practical Byzantine Fault Tolerance (PBFT) consensus for simultaneously optimising latency, energy consumption, and security. For efficient microservice orchestration and task off-loading, the containerised architecture utilises Kubernetes with Hyperledger Fabric. The experiments conducted in urban, suburban, and highway scenarios confirmed that the proposed framework outperformed baseline algorithms with end-to-end latency reduction of 30–45% while also lowering energy consumption by up to 55% under moderate-to-heavy loads. With less than 1.2 seconds per block on the blockchain consensus, the system also maintained task completion rates exceeding 95% during peak conditions. The framework demonstrates consistent performance across various vehicular densities and consumes zero-knowledge proofs with attribute-based encryption for data against cybersecurity threats. These results confirm that the integration of DDQN and blockchain technology effectively tackles primary obstacles IoV faces by providing secure edge computing for next generation vehicular networks.
Efficiently verifying mathematical proofs and computations has been a heavily researched topic within Computer Science. Particularly, even repetitive steps within a proof become much more complex and inefficient to validate as proof sizes grow. To solve this problem, we suggest viewing it through the lens of Incrementally Verifiable Computation (IVC). However, many IVC methods, including the state-of-the-art Nova recursive SNARKs, require proofs to be linear and for each proof step to be identical. This paper proposes Lova, a novel framework to verify mathematical proofs end-to-end that solves these problems. Particularly, our approach achieves a few novelties alongside the first-of-its-kind implementation of Nova: (i) an innovative proof splicing mechanism to generate independent proof sequences, (ii) a system of linear algorithms to verify a variety of mathematical logic rules, and (iii) a novel multiplexing circuit allowing non-homogeneous proof sequences to be verified together in a single Nova proof. The resulting Lova pipeline has linear prover time, constant verifying capability, dynamic/easy modification, and optional zero-knowledge privacy to efficiently validate mathematical proofs. We offer potential use cases for Lova to secure entire Cyber-Physical Systems (CPS) pipelines, as well as localized CPS systems in automotive and healthcare devices. Code is available at https://github.com/noelkelias/lova.
With the rapid advancement of technology, cloud computing has emerged as the most popular and promising service platform. A cloud user can delegate heavy computation tasks to cloud servers. To ensure the correctness of outsourced processing (e.g., machine learning and data mining), the cloud server must prove that the processing has been executed properly. However, even without malicious intent, it is possible for a cloud server to produce incorrect results. Consequently, clients may outsource the same task to multiple cloud servers and receive various results, aiding them in selecting the best outcome. To protect data privacy, the cloud server must encrypt the results before sending them back to the user. Yet, processing and verifying encrypted results remain significant challenges. To avoid the expensive computational overhead of decrypting ciphertexts from cloud servers one by one, clients prefer to use homomorphic encryption (HE) to obtain the combined output from a single server. However, existing schemes fall short of efficiently verifying the correctness of computations over encrypted data processed by multiple cloud servers, especially in extracting the results computed by each server. In this paper, we introduce a new framework for verifiable outsourced computing systems. In this system, each cloud server's computation result is protected by Paillier encryption, and the edge server can verify these results using zero-knowledge proofs and aggregate the verified ciphertexts. The client can extract the combined plaintext through the Base-3 conversion algorithm to identify each cloud server's results and any non-participating servers. We also prove the security of our scheme and analyze its performance from both theoretical and experimental aspects. Performance analysis shows that our system significantly reduces the client's workload and is userfriendly
Gang Xu, Shiyuan Xu, Xinyu Fan, Yibo Cao · 7 authors
The Industrial Internet of Things (IIoT) has been widely studied, which dramatically enhanced the manufacturing efficiency and service elasticity. However, how to ensure the data confidentiality and security in the event-driven publish/subscribe communication model becomes a cumbersome problem. To address this concern, ring signatures have been researched deeply. Nevertheless, existing solutions have large computational burdens and neglect to incorporate reporting and tracing features, which makes it impractical for IIoT. In this way, research focus on designing an efficient report and traceable ring signature is still far-reaching. In this article, we propose RAT ring, a novel report and traceable ring signature, which provides publisher authentication, anonymous communication, reporting, and tracing. To achieve this, we adopt the zero knowledge proof to verify the authenticity of publisher data, and the signature of knowledge to trace the signature. Then, we formalize and prove the security of our scheme. Eventually, through comprehensive performance evaluation, our scheme outperforms prior works by approximately up to 51 times in terms of total computational overhead. These results demonstrate that our design is practical and effective for data privacy-preserving in IIoT.
Alfonso Egio, Álvaro Le Monnier, Muhammad Asad, Maxime Compastié · 5 authors
As the sixth generation of the cellular network is set to be deployed around 2030, the needs for ubiquitous connectivity and increased resource demand will press for further collaboration between different stakeholders to constitute an efficient and resilient network fabric. In practice, the deployment of multiple network slices in multiple domains is one of the promising approaches to reach this vision. However, from a privacy standpoint, this introduces additional risks for the customers, as a malicious slice may contemplate the impersonation of a legit one for exfiltrating network traffic. It is therefore necessary to proceed with slice identification and authentication to prevent any collaboration with a non-legit network domain while avoiding exchanging sensitive data before authentication. In response to this challenge, this paper presents a privacy-preserving authentication framework for inter-slice communication. The framework integrates Zero-Knowledge Proofs (ZKPs) for privacy-preserving authentication and Public Key Cryptography (PKC) for secure identity management, ensuring that no sensitive information is jeopardized before a slice can be trusted. We expose an implementation prototype and evaluate it in controlled slicing environment, demonstrating its ability to maintain performance under varying operational constraints. Quantitative results highlight the efficiency limited resource consumption of the authentication model, its scalability in distributed environments, and robustness against security threats.
Ensuring privacy and security in healthcare data sharing is critical due to the sensitive nature of patient information and the growing threat of cyber attacks. This paper explores the development of privacy-preserving data-sharing protocols for healthcare systems by integrating cryptographic techniques and blockchain technology. The study aims to establish a secure framework that facilitates seamless data exchange among healthcare stakeholders while maintaining data integrity, confidentiality, and access control. Key cryptographic mechanisms, including homomorphic encryption, zero-knowledge proofs, and attribute-based encryption, are employed to ensure that only authorized entities can access patient records without exposing sensitive details. Blockchain technology is leveraged to create a decentralized and tamper-resistant ledger, ensuring transparency and auditability in data-sharing transactions. Smart contracts are utilized to enforce predefined access policies automatically, enhancing security and compliance with regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). The findings indicate that the proposed framework significantly mitigates risks associated with unauthorized access, data breaches, and single points of failure. Comparative analysis with traditional centralized systems demonstrates improved efficiency, scalability, and security in healthcare data management. The integration of blockchain and cryptographic techniques ensures robust privacy-preserving mechanisms without compromising accessibility or interoperability. This research provides a novel approach to secure data sharing in healthcare, fostering trust among stakeholders while ensuring compliance with privacy regulations. Future work will focus on optimizing computational efficiency and addressing scalability challenges to facilitate widespread adoption in real-world healthcare ecosystems.
Web-based credit card payments require complete disclosure of all payment card details for transaction authorization. The card’s CVV (Card Verification Value) is the secret code that authorizes card not presented transactions. Currently, all payment card details must be shared among various intermediaries involved in processing the transaction. To mitigate the risks associated with fraudulent transactions, industries have adopted security standards such as the PCI DSS. Credit card data confidentiality rests on all involved stakeholders adhering to best security practices, including data communication encryption, and do not misuse the payment information. However, this security posture does not prevent potential credit card data leaks. We propose an alternative method for conducting remote card payments that does not require disclosing the authorization code while ensuring high interoperability with existing payment networks. Our approach demonstrates how designated verifier Zero-Knowledge Proofs (ZKP) enable minimal disclosure of card details, particularly protecting the confidentiality of authorization codes.
Daria Schumm, Cedric von Rauscher, Katharina Olga Emilia Müller, Burkhard Stiller
Transparency and immutability of blockchains can expose metadata and raise concerns about its classification as personal data under privacy regulations. This paper investigates privacy risks associated with metadata in blockchain-based identity systems. Additionally, two privacy-preserving mechanism designs, namely Zero-Knowledge Proof (ZKP) and Homomorphic Encryption (HE), to protect metadata are proposed. As a result, this work introduces the first use case of HE privacy-preserving mechanism in the context of Decentralized Identity (DI) and Self-Sovereign Identity (SSI) systems.
Efficiency and interoperability are essential for the evolution of secure and scalable e-governance systems. This paper presents a novel framework for interoperable smart contract generation that integrates semantic technologies, and Layer-2 blockchain scaling solutions to enhance interoperability, security, and efficiency in e-governance applications. Using zero-knowledge proofs for privacy-preserving transactions and self-sovereign identity mechanisms for decentralized authentication, the proposed architecture ensures trust and compliance with international standards. Initially applied to e-voting, this framework is adaptable to broader public services, fostering a transparent, cost-effective, and sustainable digital governance ecosystem.
The digital economy is one of the most dynamic, fastest growing, and impactful domains in modern economic development. To fully exploit the potential value of data as an economic resource, many governments have implemented policies to encourage and regulate data circulation and trade. Despite the increasing adoption of blockchain-based data trading platforms, a significant challenge persists: verifying data quality without compromising privacy. This deficiency reduces data utilization efficiency and limits its economic potential We propose HES, a decentralized scheme for data quality verification in transactions, which integrates homomorphic encryption, blockchain, and zero-knowledge proofs to balance privacy and verifiability. The proposed method introduces data meta-certificates to represent the original data and employs cryptographic methods to enable zero-knowledge verification. This mechanism supports the verification of both data content and quality while preserving privacy. Data buyers can verify datasets without accessing specific content or involving third-party intermediaries. In this decentralized framework, data buyers can independently verify whether a dataset meets their requirements based solely on its content and quality indicators. Our experiments show that the proposed approach solves the challenge of reaching data quality consensus in distributed scenarios, enabling data buyers and sellers to swiftly align on quality standards. This facilitates the secure circulation and trade of data assets while unlocking their economic value. The findings offer a robust, privacy-preserving consensus mechanism for evaluating data quality in transactions.
The Artificial Intelligence of Things (AIoT) is driving human society from “information” to “intelligence”, and the information technology industry is undergoing tremendous changes. However, AIoT data faces security threats such as leakage and illegal access when assisted by third parties. Therefore, some scholars use attribute-based proxy re-encryption (ABPRE) for secure sharing of data. However, the existing ABPRE schemes suffer from high computational overhead and inefficient attribution revocation, which seriously hinders practical application. To solve these problems, in this paper, we propose an efficient attribute-based proxy re-encryption scheme with fast attribute revocation (EPREAR). We design a non-interactive zero-knowledge proof protocol based on blockchain to ensure the verifiability of the key during attribute revocation. Furthermore, we devise a boundless encryption and decryption mechanism to enable the system's encryption and decryption with a fixed computation overhead, regardless of the size of the attribute set. And EPREAR possesses the ability to add infinite attributes without re-initializing the system. Finally, we perform theoretical and experimental analyses that show EPREAR has excellent computational performance. As a consequence, it has better application value in AIoT.
Management of secret keys for digital signatures is one of the most critical issues in decentralized applications. Since there is no administrator, losing a secret key can result in losing all assets or rights. To address this problem, fuzzy extractors and fuzzy signatures, which generate private keys directly from biometric information, have been considered in addition to conventional biometric authentication. However, these methods using biometric secret keys do not support group signatures. Therefore, it is not applicable to use cases that require consensus building by a specific community (group), such as DAO and DeFi.In this paper, we propose a new scheme for converting existing fuzzy signatures to group signatures using zero-knowledge proofs to address this problem. More precisely, we first define an anonymizable signature that is a generalization of a group signature and then convert a fuzzy signature into an anonymizable signature using an ordinary (classical) zero-knowledge proof. In addition, the signature data size is optimized to a constant size using zk-SNARK. Our implementation experiments show that our schemes achieve practical signature generation and verification times and signature sizes even for a group of up to 100,000 people. This paper’s results can be used to prevent the loss of secret keys and enable flexible DApps use cases.
Nai‐Wei Lo, Chi-Ying Chuang, Jheng-Jia Huang, Yuxuan Luo
With the rise of the Internet of Vehicles (IoV), secure and efficient authentication is essential to prevent cyber threats. This paper proposes a session key establishment protocol using Zero-Knowledge Proofs (zk-SNARKs) and Elliptic Curve Cryptography (ECC), including the Elliptic Curve Diffie–Hellman (ECDH) key exchange, to ensure privacy and efficiency. While zk-SNARK computations introduce additional verification overhead, our optimizations, such as precomputed proof parameters and lightweight session re-authentication, mitigate delays. Performance evaluation shows a 20% reduction in computation overhead and a 75% faster re-authentication time compared to existing methods, making it a secure and practical solution for real-world IoV applications.