Blockchain Papers

Koji Nuida

No abstract is available for this record.

Yaman Jandali, Ruisi Zhang, Nojan Sheybani, Farinaz Koushanfar

Privacy-preserving technologies have introduced a paradigm shift that allows for realizable secure computing in real-world systems. The significant barrier to the practical adoption of these primitives is the computational and communication overhead that is incurred when applied at scale. In this paper, we present an overview of our efforts to bridge the gap between this overhead and practicality for privacy-preserving learning systems using multi-party computation (MPC), zero-knowledge proofs (ZKPs), and fully homomorphic encryption (FHE). Through meticulous hardware/software/algorithm co-design, we show progress towards enabling LLM-scale applications in privacy-preserving settings. We demonstrate the efficacy of our solutions in several contexts, including DNN IP ownership, ethical LLM usage enforcement, and transformer inference.

Tanuj S. Rohankar, Vijay S. Gulhane

The digital forensic investigation process overwhelmingly depends on the unbroken, tamper-proof, and audit able Chain of Custody for evidence data. However, most traditional Chain of Custody systems suffer limitations being either static permission control, weak traceability, or even worse lack implementation of cryptographically enforced privacy and integrity guarantees in evidence lifecycle management. These failures can disable real-time, transparent, and secure evidence life cycle management, especially in costly, heterogeneous, and multi-party environments. The work introduces FAIR-CoC, a Forensic Adaptive Integrity and Reputation Chain-of-Custody system conceptually based on the Hybrid Blockchain-IPFS CoC Ledger (HBI-CoC) architecture to resolve these problems. This system uses IPFS as a decentralized repository for forensic artifacts while using private Ethereum blockchain for immutable record keeping of cryptographic hashes, access metadata, and smart contract logic. Furthermore, the framework consists of five key parts, ZK-TIV (Zero-Knowledge Temporal Integrity Verifier) enables evidence access within permissible timestamp windows using zk-SNARK proofs without revealing accessor identities to enhance privacy and temporal accountability in the process; the MV-PGChain (MultiVector Provenance Graph Chain) builds high fidelity provenance graph capturing handler, location, tool, and timestamp changes, with Merkle root snap-shots anchored on-chains; AWReS (Access Weighted Reputation Scorer) dynamically assesses trustworthiness for custodians using on-chain behavioral analytics; HAT-FSS (Homomorphic Audit Tags for Forensic Shard Storage) allows encrypted auditability for IPFS-stored shards using homomorphic verification tags; PA-ESC (Predictive Access Escalation Smart Contracts) embeds AI-based access behavior modeling to automate privilege revocation or escalations. Collectively, these functionalities present a novel adaptive and privacy-preserving CoC framework with solid integrity, traceability, and trust guarantees. Experimental evaluations contend with low latency and accuracy whether across all modules, establishing FAIR-CoC as a leap forward toward secure, scalable, and intelligent forensic chain-ofcustody systems.

Xiaoyu Zhou

With the acceleration of urbanization and the promotion of the “dual carbon” goal, the road transport system is facing the triple challenges of efficiency bottlenecks, excessive carbon emissions, and data security risks. In view of the shortcomings of the existing research in dynamic response, multi-objective collaboration and privacy protection, this paper proposes a three-in-one intelligent management framework: (1) construct a real-time dynamic path optimization model based on Deep Reinforcement Learning (DRL), and realize the precise regulation of traffic flow through multi-source data fusion and adaptive reward mechanism; (2) Design a multi-objective optimization model integrating carbon trading mechanism to quantify the synergistic relationship between transportation efficiency, carbon emissions and economic costs; (3) Develop a distributed data management framework based on blockchain, and use zero-knowledge proof and smart contract technology to protect user privacy. The peak simulation experiment based on the fifth ring road section of Beijing shows that the proposed method reduces the average traffic time by 18.7%, the carbon emission by 23.5%, and the risk of data leakage by 76% compared with the traditional algorithm. This study provides theoretical and technical support for the construction of a safe, efficient and low-carbon intelligent transportation system.

J Wang, Xiaosong Guan, Changxin Gao, Shijuan Yang

In the Internet of Vehicles (IoV) network, numerous vehicle terminals are required to continuously upload local data to maintain the latest service models, which supports intelligent transportation and personalized services. However, the privacy risks posed by this continuous data uploading cannot be ignored. Federated learning, as a distributed ma-chine learning paradigm, enables global model training without sharing original data. The introduction of blockchain further supports decentralization and immutability. However, federated learning also faces the risk of poisoning attacks, where malicious clients may upload abnormal or tampered model updates, severely impacting global model performance. To address this, this paper proposes a security framework that combines zero-knowledge proofs, federated learning, and blockchain. Clients use zero-knowledge proofs to ensure the legitimacy of uploaded updates, while the blockchain is responsible for verification and storage. Ultimately, a robust global model is obtained through federated aggregation. Experimental results demonstrate that this scheme effectively resists poisoning attacks, significantly improving system security and reliability while protecting user privacy.

Vivek Pandey, Sahil Ambekar, R.Sunil Varma, Kunal Nandiwadekar · 6 authors

India’s democracy allows citizens to make representatives accountable during elections, but no immediate mechanism enables them to remove representatives prior to a particular term ending if representatives are seriously underperforming or categorized as nonrepresentative. The Right to Recall (RTR) E-Voting System utilizing Blockchain Technology addresses this lack of an immediate mechanism, providing a safe, transparent, and performance-based recall mechanism that decades of political science literature have deemed necessary. Recalling representatives for statutory reasons is often a slow bureaucratic process that is influenced by political bias when it is allowed at all. Blockchain’s decentralized, immutable, and secure nature helps guarantee that votes are tamper-proof, verifiable, and transparent. The system employs smart contracts to automate much of the recall processes while reducing human intervention and our potential for manipulation. Existing cryptographic techniques such as Elliptic Curve Cryptography (ECC) and Zero-Knowledge Proofs (ZKP) guarantee a high level of security and voter anonymity in our proposed system. This paper describes the design, architecture, implementation challenges, and the impact of a blockchain technology-enabled RTR voting platform in the context of India.

Manoj Patil, Adarsh Mote, Diksha Patil, Pratik Patil · 5 authors

Blockchain technology presents a transformative solution to the vulnerabilities inherent in traditional electoral processes by ensuring security, transparency, and scalability. By leveraging a decentralized and immutable ledger, blockchain provides vote integrity, voter anonymity, and end-to-end verifiability. This paper details the design, implementation, and evaluation of a blockchain-based online voting system using Ethereum smart contracts. The system integrates sophisticated cryptographic techniques, including zero-knowledge proofs and homomorphic encryption, alongside a distributed architecture to enhance election integrity. Our implementation demonstrates several key advantages: tamper-proof voting records, real-time verifiability of election progress, and scalability for large electorates. Performance analysis reveals an 85% reduction in vote counting time compared to traditional methods, with 99.99% accuracy and zero security breaches during testing. The system achieves cost efficiency through automated vote tallying while maintaining voter privacy. While challenges such as regulatory compliance and user adoption persist, this work demonstrates that blockchain technology holds the potential to revolutionize democratic processes by offering a secure, transparent, and accessible voting solution for the digital age.

Oleksandr Kuznetsov, Anton Yezhov, Alexandr Kuznetsov, Valentyn Chernushevych · 5 authors

No abstract is available for this record.

Tung-Tsun Lee, Shyi-Tsong Wu, Yao-Jen Liang

With the development of science and technology, the Internet of Things (IoT) had been integrated into the daily life of people. That makes the security of IoT a necessity and gains more attention. The Device authentication is an important issue in the security of IoT. In this paper, we propose a device authentication scheme based on both Physically Unclonable Function (PUF) and zero-knowledge proof. The proposed mutual authentication scheme reduces the memory load on the server and provides both data integrity and confidentiality during the authenticating process. We verify the proposed device authentication algorithm on the IoT platform Raspberry Pi using SRAM-PUF. The experimental results reveal that the proposed device authentication scheme is novel for IoT. It can resist brute force attack, replay attack, man-in-the-middle attack, machine learning attacks, and etc.

Dmytro Ovsianko, Elena Nyemkova

The deployment of distributed digital twin systems in sectors such as healthcare, manufacturing, and critical infrastructure has significantly heightened the importance of data privacy. These systems interact with numerous devices and users, increasing the risk of data leakage or unauthorized access to sensitive information. Traditional centralized identity management and access control mechanisms no longer meet the scalability, autonomy, and privacy requirements of modern distributed architectures. This article explores how smart contracts operating in blockchain environments can provide decentralized access management for digital twin systems. Smart contracts enable transparent and reliable enforcement of access policies without relying on centralized authorities. The study examines the integration of modern cryptographic technologies into smart contract workflows, including zero-knowledge proofs, decentralized identifiers (DIDs), and confidential computing. These technologies make it possible to verify access rights and perform secure operations without revealing sensitive data. The article also analyzes the limitations of existing solutions, such as the high transaction costs of public blockchains, the limited performance of traditional smart contracts, and the challenges of integrating confidential computing into resource-constrained devices. The authors outline future research directions, including optimizing Layer 2 architectures to improve performance, developing secure auditing mechanisms, and ensuring compatibility with self-sovereign identity systems. The conclusions emphasize that privacy should be treated as a fundamental property of digital twin systems. In these environments, smart contracts must serve not only as governance logic but also as trusted agents that guarantee compliance with access policies and regulatory requirements in decentralized ecosystems.

Zhaoqian LIU

Sustainable trade requires verifiable, granular, and trustworthy data across multi-jurisdictional supply chains. This paper argues that blockchain’s binding constraints are institutional, not technical, and proposes the Green Trade Blockchain Governance Trilemma: no design can simultaneously maximize (i) transactional efficiency, (ii) regulatory verifiability, and (iii) decentralized governance with commercial privacy. Comparative cases—TradeLens, IBM Food Trust, Everledger, and Power Ledger—show divergent institutional choices and outcomes: TradeLens faltered under perceived hegemonic control; Food Trust succeeded via a buyer mandate; Everledger thrived through symbiosis with trusted authorities; Power Ledger scaled within a regulatory sandbox. We further analyze the Oracle Problem as the key limit to verifiability and assess privacy-enhancing technologies, especially zero-knowledge proofs, as partial mitigations that protect sensitive data while enabling compliance checks. We conclude that success hinges on context-specific institutional design—certified oracles plus verifiable computation—rather than a one-size-fits-all stack, offering actionable guidance for policymakers, consortia, and firms building credible green-trade infrastructure.

Valeriia Balatska, Nazarii Dmytriv

The article addresses the issue of ensuringconfidential exchange of personal data in inter-organizationalinformation systems under conditions of increasing digitalinteraction between public and private sector entities. It is notedthat centralized models for processing and exchanging personaldata fail to provide an adequate level of protection againstunauthorized access, transaction tampering, and do not ensuresufficient transparency of data operations. These limitationshinder full compliance with regulatory requirements, particularlythe provisions of the General Data Protection Regulation(GDPR), ISO/IEC 27001 and 27701 standards, as well asnational legislation on information protection. The study substantiates the feasibility of using a permissioned blockchain as the architectural basis forimplementing a secure, decentralized exchange of personal datawith guaranteed access control, transaction audit, and dataimmutability. A conceptual model of the information system isproposed, involving smart contracts for managing data subjectconsent, access control, and the integration of the InterPlanetaryFile System (IPFS) for robust off-chain data storage. The modelalso includes the use of Zero-Knowledge Proof (ZKP) cryptographic mechanisms and behavioral verification criteriafor transactions. Particular attention is given to risk analysis associated withpersonal data processing in inter-organizational environments, and to the application of supplementary protection tools—suchas masking, pseudonymization, and data perturbation—tomitigate potential losses in the event of data leakage. A set oftechnical and organizational compliance criteria withinternational and national information security standards isoutlined. The aim of this research is to design an architectural modelfor inter-organizational personal data exchange based onpermissioned blockchain that ensures confidentiality, integrity, controlled access, and regulatory compliance in the field ofinformation protection.

Enze Zhou

Securities trading systems have settlement efficiency, audit transparency, and fraud prevention concerns due to centralized intermediaries and aging infrastructure. Existing research models risk counterparty trading due to delayed settlements, opaque record keeping, and human compliance checks. The study aims to design and evaluate a blockchain-based equities trading platform for transaction security and traceability. Provable Atomic Consensus for Trading (PACT), a blockchain-based architecture for regulated financial institutions' trading environments, combines hybrid consensus with a privacy-preserving cryptographic approach. A hybridized consensus process for efficient transaction finality, zero-knowledge proof enabled atomic settlements for instant delivery vs. payment while protecting commercial secrecy, and regulator-accessible smart contracts for real-time compliance checks are used in the PACT algorithm PACT found a 20% reduction in consensus finality time, 53% reduction in proof verification time, 56% improvement in smart contract vulnerability, and 42% improvement in auditability index on a permissioned blockchain with hardware-accelerated smart contracts. The study indicated 35.6% lower throughput and 41.7% lower Tx volume over 10 validators. Latency over 10 validators is 24% lower and Tx volume is 23.2% lower than existing research models. Blockchain improves securities infrastructure speed, reliability, and transparency without affecting compliance, according to studies.

Xinyi Wang, Zhang Minwen

No abstract is available for this record.

Manas Patil, Soham Rane, Ansh Shah, Narendra Shekokar · 6 authors

Zero-Knowledge Proofs (ZKPs) enable users to prove knowledge of certain information without disclosing the information itself. Multi-ID ZKP systems extend this concept, allowing individuals to submit anonymous yet verifiable com plaints across various platforms while maintaining privacy and accountability. This paper explores the integration of ZKPs into complaint management systems, addressing the challenges of anonymity, verifiability, scalability, and computational efficiency. By reviewing existing literature on ZKP applications in authentication, identity management, and scalable systems, we identify key advancements and research gaps. This work aims to establish a foundation for implementing robust, privacy preserving, and efficient complaint systems leveraging multi-ID ZXP mechanisms.

Jing Zhan, Xin Liu, Zhiqiang Zeng, Le Xiang

No abstract is available for this record.

Antoine Bak, Guilhem Jazeron, Pierre Galissant, Léo Perrin

In recent years, many hash functions have been introduced to satisfy the pressing need of some zero-knowledge protocols for such primitives allowing a low degree verification of their round function when arithmetized over a large field.While this can be achieved by restricting their sub-components to low-degree functions (and their inverse), the newest primitives in this category also leverage the intricacies of some proof systems to use “Split-and-Lookup” non-linear functions that essentially apply a small S-box in parallel over the binary representation of a field element.Such components excel at hindering attacks relying on polynomial system solving, but they offer poor security against statistical attacks. On the other hand, low degree monomials offer the opposite guarantees, being strong against statistical attacks. Several primitives have recently been proposed that combine such components in different ways in order to get the best from both.In this paper, we target such primitives by relying on the low degree components to allow a low-cost polynomial solving step. The weakness of Split-and-Lookups against linear attacks is used to simplify these systems, and their weakness against differential attacks is then used to propagate across many rounds the differential patterns obtained during polynomial solving. We instantiate this general approach by attacking round-reduced Monolith, and providing a distinguisher on full-round Skyscraper. These result then shed some light on how to best combine the different types of components to achieve the highest security.

Ali Farhani

The exponential growth of the electric vehicle (EV) industry, driven by decarbonization goals and energy transition policies, has intensified the need for sustainable and transparent supply chains. Lithium-ion batteries (LIBs), the cornerstone of EVs, pose complex life cycle challenges related to ethical sourcing, environmental degradation, traceability gaps, and inefficient end-of-life (EOL) management. Addressing these multifaceted issues requires an integrated technological approach. This study proposes a unified framework leveraging Digital Product Passports (DPPs) and blockchain technology to enable real-time, tamper-proof tracking of battery materials, components, and performance metrics throughout their lifecycle.The paper further integrates machine learning, with a focus on reinforcement learning (RL), to optimize logistics and predictive maintenance based on dynamic supply chain data. To ensure privacy and regulatory compliance in data sharing, the framework incorporates zkSNARKs—a zero-knowledge proof system that preserves confidentiality while maintaining verifiability across distributed networks. This triadic approach promotes lifecycle transparency, supports circular economy goals through efficient material reuse and recycling, and reduces the total cost of ownership (TCO) for EV stakeholders.The proposed solution addresses critical industry challenges—such as counterfeit components, low recycling efficiency, and supply chain opacity—while offering scalable applications in adjacent sectors like consumer electronics and renewable energy. The integration of DPPs, blockchain, and AI-based optimization establishes a resilient, interoperable infrastructure that enables enhanced sourcing, sustainability, and collaborative innovation in the evolving EV ecosystem.

Jannis Kiesel, Jonathan Heiss

Ensuring the integrity of business processes without disclosing confidential business information is a major challenge in inter-organizational processes. This paper introduces a zero-knowledge proof (ZKP)-based approach for the verifiable execution of business processes while preserving confidentiality. We integrate ZK virtual machines (zkVMs) into business process management engines through a comprehensive system architecture and a prototypical implementation. Our approach supports chained verifiable computations through proof compositions. On the example of product carbon footprinting, we model sequential footprinting activities and demonstrate how organizations can prove and verify the integrity of verifiable processes without exposing sensitive information. We assess different ZKP proving variants within process models for their efficiency in proving and verifying, and discuss the practical integration of ZKPs throughout the Business Process Management (BPM) lifecycle. Our experiment-driven evaluation demonstrates the automation of process verification under given confidentiality constraints.

Abhijeet Deshmukh, Vivek Mahale, Ashok T. Gaikwad

Abstract: Homomorphic encryption (HE) enables secure computations on encrypted data without decryption, offering a transformative solution for privacy-preserving computation. This review presents a ten-year retrospective (2014–2024) on HE’s evolution since Gentry’s 2009 fully homomorphic encryption (FHE) scheme, which introduced the concept of performing arbitrary computations on ciphertexts. Early schemes were hindered by inefficiencies like computational overhead and noise accumulation. Over the past decade, significant advancements have addressed these barriers. Schemes such as BGV, BFV, and CKKS have been developed for efficient integer and approximate real-number computations. Algorithmic innovations like optimized bootstrapping and improved noise management have reduced complexity. Hardware acceleration using GPUs and FPGAs has enhanced performance, while integration with secure multi-party computation and zero-knowledge proofs has broadened HE’s applicability. Applications now span privacy-preserving machine learning, genomic data analysis, and financial analytics. Toolkits such as SEAL, HElib, and PALISADE have improved accessibility for developers and researchers. Despite progress, challenges remain, including balancing efficiency and security, and improving usability for non-experts. The article also explores HE’s reliance on lattice-based problems like Learning With Errors (LWE) and Ring-LWE, which provide quantum resistance. As hybrid cryptographic models emerge, HE is increasingly recognized as a key component in securing sensitive data in the postquantum era. This review highlights HE’s maturation from a theoretical concept to a practical solution, demonstrating its potential as a cornerstone for secure, privacy-preserving computing across industries.

Rabees Paroshan, Srivaitheeswari.M, Abhishek Kumar.S.A, S. Pavithra

Quantum computing has positioned itself as a serious threat to traditional cryptography, undermining the very foundation of present-day methods of transaction and the popularly used digital payment systems. Here lies an interest in proposing a platform that can remedy these quantum-age risks present in payment mechanisms. The intended aim thus becomes that of building a secure and scalable payment system that uses quantum computing algorithms to train AI models for fraud detection in real-time, QKD to manage key security, and PQC to securely encrypt transaction data. For privacy, ZKP will be used to verify the transaction without revealing any details from it. Also, the platform will integrate multichain blockchains with quantum sharding, allowing separate processing, and distribution of transaction storage. Experimental results have shown that the proposed platform can resist quantum attacks, attain more accuracy in ratio detection, and improve the transaction speed more than the average blockchain solutions. The novelty of this research stands on the enhanced multichain blockchain architecture and improved Zero-Knowledge Proof protocols, which improve scalability and privacy. By bringing multichain architecture, quantum computing, QKD, and PQC into one model, this platform sets the benchmark for a secure, scalable, and affordable digital payment system.

Ravi Khatri, Prateek Pandey, Rahul Pachauri

This research tackles the challenges of non-independent and identically distributed (non-IID) data, socio-political inequalities in decentralized energy networks, and the unpredictable nature of renewable energy sources. It integrates blockchain technology with federated learning (FL) and game theory. Cluster-based FL paired with Shapley value allocation helps mitigate data heterogeneity, while a combined Stackelberg-Shapley model implemented via smart contracts facilitates adaptive pricing strategies. To safeguard user privacy, the system incorporates zero-knowledge proofs, differential privacy$(\varepsilon=0.5)$, and CKKS-based homomorphic encryption, achieving a 98% resistance rate against cyberattacks. Field tests in the EU's NER400 sandbox and blockchain-enabled microgrids in Kenya confirm the framework's effectiveness—achieving a 4.2% mean absolute percentage error (MAPE) in forecasting (improving from a 12% benchmark), curbing renewable energy certificate (REC) fraud by 89%, and cutting rural energy expenses by 40%. Leveraging a hybrid consensus model (PBFT with Sharding), the platform supports over 10,000 per second with sub-second latency, bridging interoperability gaps between Ethereum-based REC systems and Hyperledger platforms.

Mutahar Mujahid Mohammed, Hemasree Koganti, Abdul Hadi, Sai Krishna Akula · 6 authors

Traditional and digital voting systems both have their flaws, such as being vulnerable to fraud, having limited auditability, and being controlled by a central authority, which poses a growing threat to the honesty, openness, and safety of elections. This study seeks to solve the problem by exploring the potential of a voting system built on the blockchain that would guarantee voter anonymity, eliminate single points of failure, and offer end-to-end verifiability. A hybrid blockchain architecture is proposed, combining permissioned networks for high performance with public blockchain anchoring for transparency and fairness. A prototype implemented on Hyperledger Fabric was evaluated through simulated municipal elections with 10,000 virtual voters, achieving an average vote processing latency of 0.75 seconds, throughput of 4,000 votes per minute, 100% vote integrity, 99.97% system uptime, and full voter anonymity via zero-knowledge proofs. The results confirm that the proposed system can meet the performance, scalability, and privacy requirements for secure digital elections, while identifying key challenges—such as scalability, regulatory compliance, and digital inclusion—that must be addressed for real-world deployment.

Rui Han, Bin Yuan, Weizhong Qiang, Deqing Zou · 5 authors

The widespread use of IoT devices in the accommodation and hospitality sectors has created demand for temporary device-permission sharing and transfer. Prior work has largely focused on security issues in device permission sharing, with far less attention devoted to device permission transfer. However, inappropriate access control management during device permission transfer can also lead to violations of the users' expectations of control over their devices. For example, a malicious host retaining or regaining access to a camera after its permission has been transferred to a tenant. In this paper, we present the first systematic study on understanding and enhancing the security of device permission transfer in IoT leasing. To this end, we propose Forseti, a new authorization framework that leverages zero-knowledge proof and a decentralized ledger to ensure that the rights of both hosts and tenants are not violated. Our evaluation demonstrates that Forseti is effective, efficient, scalable, and compatible with existing IoT platforms.