Blockchain Papers

Marcel Nageler, Lorenz Schmid, Maria Eichlseder

No abstract is available for this record.

Yan Watequlis Syaifudin, Vipkas Al Hadid Firdaus, Imam Fahrur Rozi, Chandrasena Setiadi · 8 authors

The digitization of health records has enhanced clinical efficiency, but amplified risks related to data privacy, integrity, and auditability.While permissioned blockchains offer immutability and traceability, they often fail to reconcile transparency with confidentiality-either exposing sensitive data or obscuring it beyond regulatory scrutiny.To address this gap, this paper presents an integrated framework that combines Zero-Knowledge Proofs (ZKPs) with a permissioned blockchain to enable verifiable yet private healthcare transactions.A visit centric Electronic Health Record (EHR) model supports three real-world use cases: medication validity, procedure confirmation, and demographic verification.A four-layer architecture decouples data, application logic, cryptographic trust, and audit logging, allowing end-to-end validation without raw data disclosure.Experimental evaluation across three ZKP libraries (snarkJS, ZoKrates, and gnark) on a synthetic dataset of 1,000 patient visits demonstrates sub-500 ms verification latency, with snarkJS selected for its ecosystem compatibility despite slower raw performance.End-to-end pipeline latency averages 1.35 s, confirming feasibility for batch workflows such as insurance claims.The system further includes a web-based auditor interface that validates tamper-evidence under off-chain attacks, bridging cryptographic guarantees with operational compliance.

Rui Song, Yubo Song, Xiaotie Deng, Bin Xiao

To harness the commercial potential of medical data, various blockchain-based data-sharing and exchange platforms have been proposed. A key challenge is accurately tracing the provenance and transformations of medical data assets throughout the exchange process. Existing methods cannot facilitate exchanges of publicly hosted datasets while maintaining fairness, as they require revealing keys to the blockchain during interactions. This paper presents MEDET, a novel data exchange scheme that ensures traceability of medical data assets while protecting data privacy and guaranteeing exchange fairness. MEDET leverages zero-knowledge proofs to securely verify transformations within medical datasets and confirm data authenticity. Unlike previous schemes, MEDET supports both simple data exchanges and detailed tracking of data transformations and transaction histories, aiding in the provenance and value assessment of medical records. Additionally, MEDET features a key-secure protocol for fair exchange without disclosing symmetric keys. Compared to existing fair exchange schemes, MEDET uniquely ensures the privacy of publicly hosted data while simultaneously upholding the exchange fairness. The security analysis of MEDET demonstrates its security and privacy properties. The evaluation of MEDET indicates that it outperforms existing schemes in tracking data transformations and facilitating exchanges.

J C Zhang, Xinyu Bai, Qijia Zhang

Federated Learning (FL) enables collaborative model training across decentralized clients while preserving data privacy. However, practical deployments are often limited by significant communication overhead and vulnerability to Byzantine poisoning attacks. Existing defenses typically rely on post-hoc anomaly detection, but executing complex distance metrics or clustering on encrypted, sparsified updates creates a substantial computational burden for the aggregation server. We present a privacy-preserving FL framework that addresses these challenges by integrating Top-ksparsification, non-interactive zero-knowledge proofs (NIZKPs), and homomorphic encryption. Instead of relying on expensive ciphertext distance computations, our architecture uses a pre-aggregation global mask sign vector, generated through majority voting, to filter anomalous updates. This mechanism treats unselected gradient coordinates as explicit zero-votes, which mitigates malicious coalitions attempting to manipulate disjoint parameter subsets. A local error feedback mechanism ensures that heterogeneous client updates align over successive training rounds. Combined with NIZKPs to enforce coordinate-wise magnitude bounds, the framework provides Byzantine resilience without increasing communication costs or compromising privacy. Evaluations on MNIST and CIFAR-10 show that our approach maintains high communication efficiency and robustness. Under a 40% malicious client poisoning attack and a 50% sparsification ratio, the framework achieves final accuracies of 92.14% and 63.20%, respectively, demonstrating its effectiveness in bandwidth-constrained, hostile environments.

Yueyue He, Wenxuan Fan, Kaiming Chen, Atsuko Miyaji · 5 authors

No abstract is available for this record.

Zongrui Tang, Xiaomei Dong, Wenpeng Chen, Hanyu Mao · 8 authors

No abstract is available for this record.

Ujjwal Mishra, Rajesh Sasane, Sachin Lad, Pallavi Bhujbal · 6 authors

No abstract is available for this record.

Nikita Tiwari, Pradeep Kumar Biswal, Prakash Ranjan

The rapid development of the Internet of Medical Things (IoMT) has also facilitated real-time monitoring of healthcare, yet creates major issues of security, privacy, and interoperability, particularly in terms of emerging threats of quantum computing. This paper introduces a quantum-resilient blockchain paradigm, which combines post-quantum cryptography (PQC), Zero Knowledge Proofs (ZKPs), and Fast Healthcare Interoperability Resources (FHIR) into secure and interoperable healthcare data management. Key encapsulation is performed using lattice-based algorithms, including Kyber and NTRU, and Dilithium and Falcon are algorithms used to secure digital signatures against quantum attacks. An authentication system which is based on a ZKP, and role-based access control allows privatizing access to electronic health records without exposing sensitive data. The framework is deployed on a PBFT-based permissioned blockchain and tested in simulated IoMT settings and has low latency, high throughput, and efficient cryptographic performance. In general, the suggested system will provide a reconfigurable, secure, and future-oriented method to safeguard the healthcare information against quantum threats without compromising the interoperability of the heterogeneous systems.

Siqin Li, Kun He, Min Shi, Yajing Huang · 6 authors

Anonymous submissions inspire people to speak up since hiding their identities can protect them from negative influence by their own words. However, the abuse of anonymity may bring harassment to those public submission callers. Existing works only handle DoS attacks or block harassment senders in an active manner, which behave poorly in the early prevention of uncharacterized harassment. In this paper, we propose MsgFliter, a sender-anonymous messaging system with proactive anti-harassment mechanism. Our core idea is to prevent unanswered senders from sending messages continually while keeping their identities, messages, and sender types secret. To meet the functionality and security requirements of MsgFliter, we propose the Anti-Harassment Anonymous Authentication (AHAA) protocol. We associate messages from the same sender through linkable tags and invalidate linkability when a message is replied to. To achieve session indistinguishability, we further combine the proposed anonymous authentication with zero-knowledge proofs of disjunctive relations. We implement MsgFliter and compare its performance with related solutions. Experimental results show that our solution is practicable.

Shailendra Singh, Prashant Mishra

No abstract is available for this record.

Mohammad Raeini

Compression algorithms and functions have been extensively utilized in various applications, e.g., in digital storage and communication. In recent years and with the popularity of machine learning applications, researchers have utilized compression techniques for addressing key problems in machine learning, e.g., for compressing artificial neural networks or for optimizing KV-cache memory in LLMs. Due to extensive applications of compression algorithms in different domains, a whole new era of innovations and applications for data compression algorithms can be envisioned. In this article, we discuss that compression functions with interesting properties, e.g., fully homomorphic compression (FHC) algorithms, can have applications beyond compressing data. FHC algorithms can potentially enable performing computation on compressed (and encrypted) data. Thus, fully homomorphic compression techniques can be used for reducing the computational and communication costs of compute and communication-intensive workloads, such as fully homomorphic encryption (FHE) & zero-knowledge proof (ZKP) applications, (secure) vector databases (VDBs and SVDBs), blockchain-based technologies, image and video processing, privacy-preserving or private LLMs, and LLM & AI inference, etc.

Matúš Maďar

Discreet Log Contracts (DLCs) enable conditional Bitcoin payments using external oracles, but their efficiency depends on cryptography, system design, and oracle interac- tion. This thesis introduces a lightweight, modular Rust framework for prototyping and benchmarking DLC components, with parsing, computation, storage, and oracle inter- faces separated. We implement optimizations for anticipation point computation, parallel processing, and Schnorr adaptor signatures, while outlining theoretical improvements. In collaboration with the community, we integrate a performant Schnorr adaptor and pro- vide, to our knowledge, the first direct comparison of Schnorr- and ECDSA-based adaptor signatures. Benchmarks show Schnorr pre-signing up to five times faster than ECDSA, with further gains from parallelism and basis optimization, demonstrating how careful design improves the practicality of DLCs for advanced Bitcoin contracts.

Ashutosh Kumar, Manik Malhotra, A. Anjali, Amrendra Singh Yadav

Vehicular Ad Hoc Networks (VANETs) require ultra-low-latency authentication in high-mobility scenarios, decentralized trust, and emerging post-quantum security threats. This paper proposes a lightweight blockchain-assisted group authentication framework that integrates post-quantum cryptography with decentralized trust management. The scheme leverages CRYSTALS-Kyber for secure group key distribution, CRYSTALS-Dilithium for message authentication, and zero-knowledge proofs to achieve conditional anonymity and unlinkability. A permissioned Hyperledger Besu blockchain logs encrypted group keys and dynamically updated roadside unit reputation scores, enabling scalable and accountable authentication without monetary transaction overhead. Formal security analysis under the Dolev–Yao adversary model demonstrates confidentiality, authentication, replay resistance, and unlinkability based on standard post-quantum assumptions. Performance evaluation using OMNeT++ and SUMO shows up to 52.7% reduction in communication overhead and a group key update latency of 1.2 ms in dense traffic scenarios, while maintaining high throughput and effective Sybil attack detection. These results highlight the practicality of the proposed framework for post-quantum secure VANET authentication.

Huyen-Trang Le, Adnan Imeri, Nazim Agoulmine

The secure, privacy-preserving sharing of genomic data across multiple institutions is a critical enabler for precision medicine, yet it remains fundamentally constrained by the identifiability and immutability of genomic data. While blockchain technologies have been proposed to provide decentralized governance, auditability, and tamper resistance for genomic data sharing, blockchain-only solutions are insufficient because they expose transaction metadata, access patterns, and smart-contract logic, leaving significant privacy risks unresolved. Zero-Knowledge Proofs (ZKPs) have recently emerged as a key cryptographic primitive for addressing such limitations, enabling verifiable access control, policy compliance, and computation correctness without disclosing sensitive genomic data. Although several surveys examine ZKPs or blockchain in isolation or across heterogeneous application domains, there is currently no dedicated survey that systematically analyzes their combined use in multi-party blockchain-based genomic data sharing systems. This paper addresses this gap by presenting a comprehensive, domain-specific survey of ZKP-enabled blockchain architectures for genomic data sharing. We classify existing approaches by architectural models, ZKP techniques, governance mechanisms, and threat-mitigation capabilities, and then compare their assumptions, performance characteristics, and deployment maturity. Furthermore, we identify open challenges in scalability, interoperability, proof overhead, and regulatory compliance, and outline future research directions for secure, scalable, and ethically compliant genomic data-sharing ecosystems.

Tuan-Dung Tran, Tran Hoang Hai, Quang Dai Hoang Phuc

No abstract is available for this record.

Michal Ľaš

Takmer všetky systémy pre správu identít, centralizované alebo decentralizované, používajú na zaistenie Sybil-rezistencie, ochrany pred útokmi, ktoré využívajú veľké množstvo falošných identít, centralizovaný prístup. Tieto systémy zvyčajne vyžadujú overenie prostredníctvom telefónneho čísla alebo dokladu totožnosti vydaného štátom. Cieľom tejto práce je navrhnúť a implementovať decentralizovaný systém pre správu identít, ktorý zabezpečí Sybil-rezistenciu, anonymitu používateľov a súkromie ich osobných údajov. Navrhované riešenie využíva overenie biometrie tváre a anti-Sybil analýzu sociálneho grafu. Pre zabezpečenie decentralizácie, anonymity a ochrany súkromia sa využívajú technológia blockchain, zero-knowledge proofs a trusted execution environments. Výsledkom je robustný systém, ktorý zabezpečuje jedinečnosť registrovaných užívateľov, ich anonymitu a súkromie bez spoliehania sa na centralizované autority. To predstavuje významný krok k dosiahnutiu samostatnej správy identít, ktorá je v dnešnom svete kľúčová, keďže osobné údaje sú cennou komoditou.

Y Zheng, Jianming Lin, Chang‐An Zhao

Abstract Bilinear pairings have emerged as a fundamental tool in public-key cryptography, enabling advanced protocols such as identity-based encryption, short signatures, and zero-knowledge proofs. This paper focuses on optimizing pairing computations on curves with embedding degree 2, addressing both theoretical foundations and practical implementations. We propose an optimized double-and-add ladder algorithm that leverages the technique of y -coordinate recovery, achieving superior performance for the Tate pairing on supersingular curves and the Omega pairing on non-supersingular curves. Our method is implemented based on the RELIC cryptographic library, demonstrating significant efficiency improvements over Miller’s algorithm. Specifically, it reduces the number of base field multiplications (respectively CPU clock cycles) by 17.53 % (respectively 13.58 %) for the reduced Tate pairing on supersingular curves with a 1536-bit field size and by 12.37 % (respectively 8.39 %) for the Omega pairing on non-supersingular curves of the same size. This work establishes the first comprehensive implementation framework for cubical-based pairing computations on curves with embedding degree 2, providing quantified optimizations for practical cryptographic deployment.

Yilai Lian, X. Liu, Lanying Liang, Wei Ye · 10 authors

No abstract is available for this record.

Dongyu Cao, Bixin Li, Huijie Zhang, Yong Wang · 5 authors

Blockchain technology improves supply chain management by ensuring the immutability of transaction records and facilitating process tracking. However, the transparency of blockchain raises significant privacy concerns, as sensitive information such as buyer and supplier qualifications, product specifications, and transaction amounts is often exposed. Compliance verification, which needs access to specific sensitive data for compliance checks, becomes challenging in blockchain-based privacy-preserving supply chains. This paper introduces ZKVeil, an innovative scheme utilizing zero-knowledge proof technology to maintain the confidentiality of sensitive information while ensuring compliance verification. Additionally, ZKVeil uses decentralized identifiers and verifiable credentials to ensure the authenticity of transaction data. A theoretical security analysis demonstrates the effectiveness of ZKVeil in safeguarding real sensitive data and ensuring compliance with regulations. To evaluate the performance of our scheme, we implement ZKVeil on a private blockchain of 100 nodes. Taking the shipbuilding supply chain transaction as an example, the experimental results demonstrate that ZKVeil incurs low gas consumption, execution time, and memory overhead.

Yang Liu (4829), Linman Li, Jinchuan Chen

No abstract is available for this record.

Hiep. L. Thi

No abstract is available for this record.

Nithi Yugan

No abstract is available for this record.

Xinqi Dong, Yijia Wang, Yijia Wang, Xu Zhang · 6 authors

No abstract is available for this record.

Tanmayi Jandhyala, Guang Gong

No abstract is available for this record.