Blockchain technology has produced effective solutions and provides security by using cryptographic tools for various applications, attracting attention from the academic community. Therefore, researchers have taken advantage of the features of blockchain technology to increase the security of the ecosystem. Recently, as the existence of quantum computers has been felt, researchers have started to benefit from post-quantum cryptography to increase privacy and security. There has been an increase in data and asset protection in post-quantum blockchain-based solutions. To the best of our knowledge, there is no comprehensive review or taxonomy that provides a complete picture of post-quantum secure structures with privacy-preserving techniques that have the potential to be used in blockchain. This paper aims to close this gap by systematically examining these approaches and revealing the deficiencies in the existing literature and the development potential in these areas. The taxonomy examines the role of blockchain technology in post-quantum cryptography and emphasizes the potential of technologies such as zero-knowledge proof to ensure privacy in post-quantum blockchain-based systems. We also review the existing literature on addressing the performance overhead, interoperability, scalability, and security challenges in implementing post-quantum cryptography in zero-knowledge proof-enabled blockchain architectures that protect against quantum computing threats. The studies are collected from journal papers in widely used academic databases between 2018 and 2024. The studies are subjected to certain elimination criteria, and 13 studies are reviewed in detail. Our approach will facilitate discussions on future research directions by proposing the accessibility of post-quantum cryptography against quantum threats to blockchain systems and solutions to the challenges that arise in the integration phase.
This research introduced a new novel “Unified Quantum-Resilient Blockchain-Zero-Knowledge Proofs Privacy Authentication Framework (QBC-ZKPAF)” to upgrade the IoT environments with greater security. To enable privacy-preserving authentication, access control, and secure communication, the framework integrates blockchain technology with Zero Trust Architecture (ZTA) and post-quantum cryptography. A hybrid Reinforcement-Lattice Blockchain KeyGen for quantum-resilient key generation, Deep Q-Network Multi-Factor Secure Key (DQN-MFSK) for dynamic selection of keys, and Zero-Knowledge Proof for privacy-preserving signatures are employed to achieve secure IoT settings. This architecture entails data privacy and confidentiality, auditability and traceability, and withstanding evolving threats, including potential threats in terms of quantum attacks. It then uses blockchain technology for recording unalterable data of identity and access management while Zero-Knowledge Proofs (ZKP) ensures authentication and verification without revealing sensitive information. By decentralizing identity management and enabling multi-factor authentication, QBC-ZKPAF provides robust security and privacy solutions for IoT networks. The experimental results demonstrate the model’s effectiveness with 98% privacy preservation, 700 TPS throughput, 0.7 J energy consumption, 0.98 quantum resilience, and 96% access control effectiveness, making it highly suitable for modern IoT and blockchain applications.
Consensus protocols used today in blockchains often rely on computational power or financial stakes - scarce resources. We propose a novel protocol using social capital - trust and influence from social interactions - as a non-transferable staking mechanism to ensure fairness and decentralization. The methodology integrates zero-knowledge proofs, verifiable credentials, a Whisk-like leader election, and an incentive scheme to prevent Sybil attacks and encourage engagement. The theoretical framework would enhance privacy and equity, though unresolved issues like off-chain bribery require further research. This work offers a new model aligned with modern social media behavior and lifestyle, with applications in finance, providing a practical insight for decentralized system development.
Aleksandra Szczegielniak-Rekiel, Krzysztof Kanciak, Jan M. Kelner
This study explored the diverse applications of zero-knowledge proofs (ZKPs) in next-generation network technologies, particularly in fifth-generation (5G) and emerging sixth-generation (6G) systems. ZKPs are cryptographic methods that enable one party to prove the validity of a statement without revealing the statement itself, thereby offering significant advantages in privacy-preserving authentication and authorization. Given these properties, ZKPs have garnered increasing research attention in contexts such as the Internet of Things (IoT), vehicular communications, and telecommunication protocols. To the best of our knowledge, this is the first study to provide a comprehensive, taxonomy-driven analysis of ZKP applications specifically designed for 5G and beyond. We categorize existing solutions according to the type of application, the underlying cryptographic technology, maturity level, and relevance to 6G. Furthermore, this paper examines how ZKPs can help mitigate various cybersecurity threats, such as distributed denial-of-service (DDoS) attacks, man-in-the-middle attacks, and location tracking. We also assess recent advancements in ZKP acceleration techniques and highlight the key implementation challenges. Finally, this study outlines promising directions for future research in this rapidly evolving field.
Open access
Physical Unclonable Functions (PUFs) and Hardware Security
Dan Bogdanov, Eduardo Brito, Annika Jaakson, Peeter Laud · 5 authors
Abstract This paper introduces a new set of privacy-preserving mechanisms for verifying compliance with location-based policies for vehicle taxation, or for (electric) vehicle (EV) subsidies, using Zero-Knowledge Proofs (ZKPs). We present the design and evaluation of a Zero-Knowledge Proof-of-Location (ZK-PoL) system that ensures a vehicle’s adherence to territorial driving requirements without disclosing specific location data, hence maintaining user privacy. Our findings suggest a promising approach to apply ZK-PoL protocols in large-scale governmental subsidy or taxation programs.
Jan 1, 2025·Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences
Empowering end consumers with transparent product-related information is seen as a promising way to drive sustainable consumption choices and counter the global sustainability challenges. To foster this endeavor, Digital Product Passports (DPPs) are a potential solution to share product-specific data across supply chains, aiding informed consumer decisions and supporting sustainability. However, DPPs often involve the collection of sensitive information about supply chain actors and their processes. Hence, this paper aims to develop a DPP prototype that provides end-consumers with increased and verified information. We utilize wallet-based identities, non-fungible tokens (NFTs), and zero-knowledge-proofs (ZKPs) to design a DPP for the textile industry that facilitates the transfer of verifiable information, lowering information asymmetries between humans and organizations and equips value-chain stakeholders with a means to verifiably share data. Our study seeks to bridge the design gap for a human-centric DPP infrastructure in IS literature by developing a sample infrastructure.
The increasing deployment of Unmanned Aerial Vehicles (UAVs) for military, commercial, and logistics applications has raised significant concerns regarding flight path privacy. Conventional UAV communication systems often expose flight path data to third parties, making them vulnerable to tracking, surveillance, and location inference attacks. Existing encryption techniques provide security but fail to ensure complete privacy, as adversaries can still infer movement patterns through metadata analysis. To address these challenges, we propose a zk-SNARK (Zero-Knowledge Succinct Non-Interactive Argument of Knowledge)-based privacy preserving flight path authentication and verification framework. Our approach ensures that a UAV can prove its authorisation, validate its flight path with a control centre, and comply with regulatory constraints without revealing any sensitive trajectory information. By leveraging zk-SNARKs, the UAV can generate cryptographic proofs that verify compliance with predefined flight policies while keeping the exact path and location undisclosed. This method mitigates risks associated with real-time tracking, identity exposure, and unauthorised interception, thereby enhancing UAV operational security in adversarial environments. Our proposed solution balances privacy, security, and computational efficiency, making it suitable for resource-constrained UAVs in both civilian and military applications.
Abstract Circuit languages like Circom and Gnark have become essential tools for programmable zero-knowledge cryptography, allowing developers to build privacy-preserving applications. These domain-specific languages (DSLs) encode both the computation to be verified (as a witness generator ) and the corresponding arithmetic circuits , from which the prover and verifier can be automatically generated. However, for these programs to be correct, the witness generator and the arithmetic circuit need to be mutually consistent in a certain technical sense, and inconsistencies can result in security vulnerabilities. This paper formalizes the consistency requirement for circuit DSLs and proposes the first automated technique for verifying it. We evaluate the method on hundreds of real-world circuits, demonstrating its utility for both automated verification and uncovering errors that existing tools are unable to detect.
Zero-Knowledge Proofs (ZKPs) are public key cryptosystem that enables to demonstrate that a statement which is known by them is correct without revealing the same to the verifier. ZKPs have moved in modern cryptographic systems, blockchain applications, decentralized finance (DeFi) and identity authentication systems. This paper explores the evolution of ZKPs and their significance as in secure and privacy preserving. We classify ZKPs into two groups namely interactive and non-interactive, discussing prominent protocols such as zk-SNARKs, zk-STARKs, Bulletproofs, PLONK, and Halo2. Each approach has advantages as efficiency, proof size, and computational overhead. The study further examines the multitude of applications of ZKPs, as privacy-enhanced blockchain transactions, zero-knowledge rollups for scalability, decentralized identity management, secure voting mechanisms, and regulatorycompliant financial systems. With advantages, possible limitations in scalability, lack of standardization, and vulnerabilities to emerging quantum computing threats. Due to the restrictions, hardware acceleration through GPUs and others, presents promising solutions, while new protocols such as PLONK and Halo2 seek to optimize performance to earlier developed solutions. Finally, we discuss the future trajectory of ZKPs. This review aims to provide an understanding of the current state of ZKP research, its applications, and the key challenges that need to be addressed to facilitate broader adoption.
Hotaru Beam is a logic puzzle which objective is to connect circles placed on a grid by drawing only lines with specified starting points and numbers of bends. A zero-knowledge proof is a communication protocol that allows one player to persuade the other that they are in possession of a certain piece of information without actually revealing it. We show that Hotaru Beam is NP-complete and present a physical zero-knowledge proof (i.e. implementable using physical items) for proving that one knows a solution to the puzzle.
Jungwon Seo, Juhui Lee, Yunjae Joo, K.-H. Lee · 6 authors
Blockchain-based E-participation systems significantly enhance transparency, data integrity, and security compared to traditional E-participation methods. However, existing systems often face challenges, such as inefficient attribute sampling in Zero-Knowledge Proof (ZKP)-based systems and the absence of effective differential reward mechanisms to distinguish between sincere and insincere participants. This paper introduces a blockchain-based E-participation framework designed to address these challenges. The proposed approach improves attribute sampling in ZKP-based systems by incorporating attribute keys, enabling efficient and secure sampling of participants without compromising privacy. This ensures that only eligible participants are selected while maintaining the integrity of the sampling process. Furthermore, the framework uses Shapley Values to implement a robust differential reward system that fairly compensates participants based on their sincerity, encouraging genuine contributions while penalizing insincere behavior. The security of the proposed framework is rigorously validated through a comprehensive security analysis, and its performance is thoroughly evaluated to demonstrate its effectiveness. Additionally, the feasibility of this approach is demonstrated through a prototype with real-world participants, highlighting its practicality and potential for deployment in E-participation systems.
The utilization of Zero-Knowledge Proofs (ZKPs) in blockchain technology enhances privacy while simultaneously preserving transparency. Given that blockchain networks frequently elicit privacy concerns owing to the inherently public nature of transaction data, ZKPs present a viable solution by enabling parties to authenticate transactions without disclosing sensitive information. This study primarily concentrates on zk-SNARKs and zk-STARKs, which represent advanced iterations of ZKPs that enhance both privacy and scalability. By analyzing established blockchain protocols, such as Zcash and Ethereum, this research illustrates that ZKPs can effectively safeguard privacy while also facilitating scalability through mechanisms such as zkrollups, which consolidate multiple transactions into a single proof, thereby alleviating congestion on the blockchain. Additionally, ZKPs enhance the verification efficiency, thereby reducing the computational burden on blockchain networks and promoting expedited transactions. However, challenges such as computational overheads and regulatory hurdles persist, hindering the widespread implementation of ZKPs. Future research endeavors should focus on overcoming these challenges by developing more efficient algorithms and collaborating with regulatory authorities to establish clear guidelines for ZKP-based systems. The potential implications of ZKPs extend beyond blockchain technology, offering substantial advantages to sectors such as finance, healthcare, and identity management, in which secure and confidential transactions are paramount. In summary, although ZKPs possess the capacity to transform privacy within decentralized networks, further advancements are required to fully harness their potential and ensure their extensive adoption.
The growing threat posed by quantum computing to traditional cryptographic systems demands a radical redesign of digital voting architectures. This review explores the development of a post-quantum blockchain voting protocol, emphasizing the integration of zero-knowledge proofs (ZKPs) to ensure data privacy, voter anonymity, and verifiable election integrity. The study synthesizes advancements in lattice-based and hash-based cryptographic algorithms capable of withstanding quantum attacks and evaluates their applicability within decentralized ledger frameworks. Particular attention is paid to the role of ZKPs—such as zk-SNARKs and zk-STARKs—in constructing tamper-resilient, end-to-end verifiable voting systems without compromising performance or transparency. Through critical analysis of recent protocols, consensus mechanisms, and deployment models, this paper identifies key design principles for scalable, secure, and inclusive e-voting infrastructures. The review concludes with strategic recommendations for transitioning from prototype systems to robust electoral frameworks in anticipation of the quantum era.
As blockchain technology continues to evolve, the pursuit of privacy has become a significant challenge. Although the transparency and immutability of blockchain are essential features, they can unintentionally expose sensitive information. This paper investigates the potential of Zero-Knowledge Proofs (ZKPs) and Secure Multi-Party Computation (SMPC) as innovative solutions to address these privacy concerns. ZKPs facilitate the verification of information without disclosing the underlying data, thereby enhancing privacy in transactions and identity verification processes. Meanwhile, SMPC enables collaborative computations while preserving the confidentiality of inputs, which is vital for industries such as finance and healthcare. Despite their potential, these technologies encounter challenges related to complexity, scalability, and regulatory compliance. This study offers a thorough analysis of ZKPs and SMPC, their applications, and the ethical implications involved, providing valuable insights into their role in creating a secure and privacy-conscious blockchain ecosystem.
Igor Calzada, Géza Németh, Mohammed Salah Al-Radhi
As generative AI (GenAI) technologies proliferate, ensuring trust and transparency in digital ecosystems becomes increasingly critical, particularly within democratic frameworks. This article examines decentralized Web3 mechanisms—blockchain, decentralized autonomous organizations (DAOs), and data cooperatives—as foundational tools for enhancing trust in GenAI. These mechanisms are analyzed within the framework of the EU’s AI Act and the Draghi Report, focusing on their potential to support content authenticity, community-driven verification, and data sovereignty. Based on a systematic policy analysis, this article proposes a multi-layered framework to mitigate the risks of AI-generated misinformation. Specifically, as a result of this analysis, it identifies and evaluates seven detection techniques of trust stemming from the action research conducted in the Horizon Europe lighthouse project called Enfield: (i) federated learning for decentralized AI detection, (ii) blockchain-based provenance tracking, (iii) Zero-Knowledge Proofs for content authentication, (iv) DAOs for crowdsourced verification, (v) AI-powered digital watermarking, (vi) explainable AI (XAI) for content detection, and (vii) Privacy-Preserving Machine Learning (PPML). By leveraging these approaches, the framework strengthens AI governance through peer-to-peer (P2P) structures while addressing the socio-political challenges of AI-driven misinformation. Ultimately, this research contributes to the development of resilient democratic systems in an era of increasing technopolitical polarization.
Saad Mutlaq Alluhaydan, Mohammed Obaid Alshammari, Yousef Jazaa Obaid Alshmilan, Ahmed Hamoud Alshammari · 12 authors
Background: The rise of AI health assistants and digital tools raises concerns about data security and consent management. Traditional systems are prone to failures and provide limited transparency in data sharing. Blockchain technology offers a decentralized, immutable, and secure solution to these issues. Aim: This narrative review critically examines the real-world implementations and security trade-offs of blockchain technology when applied specifically to health assistant audit trails and consent management, moving beyond theoretical propositions. Methods: A systematic search of peer-reviewed literature (2010-2024) was conducted across Scopus, IEEE Xplore, PubMed, and ACM Digital Library. Implementation case studies, prototypes, and theoretical frameworks were analyzed to assess technical architectures, performance metrics, and security evaluations. Results: Findings indicate an emerging landscape where blockchain proves useful for creating secure audit logs in AI decision-making and dynamic consent models using smart contracts. However, challenges persist, including performance and scalability issues, key management complexities, data linkage risks, and conflicts between immutability and regulatory requirements such as the GDPR's "right to be forgotten." Conclusion: Blockchain serves as a foundational layer to improve security and transparency in health assistant ecosystems. Its future potential relies on hybrid architectures, advanced cryptographic methods such as zero-knowledge proofs, and an awareness of the new security and operational challenges that arise. It is not merely a database but a comprehensive solution for integrity and control.
Federated Learning has emerged as a promising paradigm for collaborative machine learning while preserving data privacy. Federated Learning is a technique that enables a large number of users to jointly learn a shared machine learning model, managed by a centralized server while training data remains on user devices. In recent years, along with the blooming of Machine Learning (ML)-based applications and services, ensuring data privacy and security has become a critical obligation. ML-based service providers are not only confronted with difficulties in collecting and managing data across heterogeneous sources but also challenges of complying with rigorous data protection regulations such as the General Data Protection Regulation (GDPR) Federated Learning is very important to reduce data privacy risks. Federated Learning is a scheme in which several consumers work collectively to unravel machine learning problems, with a dominant collector synchronizing the procedure. This paper reviews recent advancements in privacy-preserving techniques for federated learning from a machine-learning perspective. This paper investigates the potential of Federated Learning for privacy-preserving machine learning in domains like healthcare, finance and IOT, where data privacy is paramount. We explore existing techniques to enhance privacy, including differential privacy, secure aggregation, homomorphic encryption, federated learning with encrypted, meta-learning, machine learning, privacy-preserving techniques, blockchain technology, decentralized learning, federated averaging, data privacy, searchable encryption and zero-knowledge proofs. This paper concludes with future research directions to address ongoing challenges & further enhance the effectiveness & scalability of privacy-preserving federated learning.
Olumide Kumuyi, Esther Uzoka, Bisola Akeju, David Excel Ozowara
The Framework for Privacy-Focused Digital Identity Verification Supporting Financial Inclusion in Africa proposes an integrated, secure, and ethically aligned model for digital identification systems that enhance access to financial services while safeguarding individual privacy. The framework addresses the dual challenge of expanding digital financial inclusion across Africa’s underserved populations and maintaining trust through data protection and regulatory compliance. It emphasizes privacy-preserving technologies such as federated identity management, zero-knowledge proofs, and biometric encryption to authenticate users without disclosing sensitive personal information. By enabling decentralized and consent-based data sharing, the model ensures individuals retain ownership of their digital identities while allowing financial institutions to verify eligibility and compliance with Know Your Customer (KYC) and Anti-Money Laundering (AML) regulations. The framework also integrates blockchain-based audit trails for transparent verification processes and tamper-proof recordkeeping, enhancing institutional accountability. It adopts interoperable standards to link national ID systems, mobile network operators, and fintech platforms, enabling seamless cross-border transactions and inclusive participation in the digital economy. A multilayer governance structure encompassing regulators, financial service providers, and civil society stakeholders promotes ethical oversight and equitable access. Furthermore, the framework supports context-sensitive deployment, accommodating infrastructural disparities and socio-cultural factors unique to African regions. It aligns with global data protection norms such as the General Data Protection Regulation (GDPR) and the African Union Convention on Cyber Security and Personal Data Protection (Malabo Convention), while encouraging local innovation in identity ecosystems. Ultimately, this privacy-centered digital identity verification framework establishes a resilient foundation for secure inclusion, reducing barriers for the unbanked, mitigating identity fraud, and fostering digital trust. By combining privacy engineering, inclusive design, and interoperable governance, it contributes to the broader agenda of sustainable digital transformation and equitable financial empowerment across Africa.
As blockchain technologies increasingly underpin significant economic and social interactions, the need for advanced security mechanisms to protect data exchanged across these decentralized networks becomes crucial. This paper introduces a novel cryptographic scheme designed specifically for blockchain ecosystems to ensure the security, integrity, and confidentiality of data transactions. Our proposed scheme leverages a combination of homomorphic encryption and zero-knowledge proofs, integrated seamlessly with blockchain's inherent properties, such as decentralization and immutability.
Xin Wang, Li Jiaqian, Ding Xueshuang, H. Zhang · 5 authors
The problem of data privacy protection in the information age deserves people’s attention. As a distributed machine learning technology, federated learning can effectively solve the problem of privacy security and data silos. Differential privacy(DP) technology is applied in federated learning(FL). By adding noise to raw data and model parameters, it can further enhance the degree of data privacy protection. Over the years, differential privacy technology based on federated learning framework has been developed, which is divided into central differential privacy federated learning(CDPFL) and local differential privacy federated learning(LDPFL). Although differential privacy may reduce the accuracy and convergence of federated learning models while protecting data privacy, researchers have proposed a variety of optimization methods to balance privacy protection and model performance. This paper comprehensively expounds the research status of differential privacy techniques based on the federated learning framework, first providing detailed introductions to federated learning and differential privacy technologies, and then summarizing the development status of two types of federated learning differential privacy(DPFL) techniques respectively; for CDPFL, the paper divides the discussion into first proposal of CDP and typical application examples, the impact of Gaussian mechanisms on model accuracy, optimization based on asynchronous differential privacy, and insights from other scholars; for LDPFL, the paper divides the discussion into first proposal of LDP and typical application examples, processing multidimensional data and improving model accuracy, existing methods and optimization for reducing communication costs, balancing privacy protection and data usability, LDPFL based on the Shuffle model, and insights from other scholars; following this, the paper addresses and summarizes the unique challenges introduced by incorporating differential privacy into federated learning and proposes solutions; finally, based on a summary of existing optimization techniques, the paper outlines future directions and specifically discusses three research ideas for enhancing the optimization effects of federated differential privacy: advanced optimization strategies combining Bayesian methods and the Alternating Direction Method of Multipliers (ADMM), integrating lattice homomorphic encryption techniques from cryptography to achieve more efficient differential privacy protection in federated learning, and exploring the application of zero-knowledge proof techniques in federated learning for privacy protection.