Blockchain Papers

Chechelnitsky, Igor

This work introduces the Adversarial Cost Model (ACM v1.0), a formal security framework unifying computational, economic, and physical attack costs in a single rational adversary model. Unlike traditional security models based purely on computational hardness, ACM evaluates real-world feasibility of attacks under post-quantum cryptography, behavioral authentication, zero-knowledge proof systems, and decentralized governance. The model formalizes adversarial actions through total cost functions combining time complexity, hardware requirements, capital liquidity, and physical laboratory constraints. Multiple critical attack classes are analyzed, including hybrid side-channel + Grover attacks, GAN-based behavioral cloning, flash-loan Sybil governance attacks, post-quantum brute-force exhaustion, and zero-knowledge proof forgery. The results demonstrate that many real-world system failures arise not from cryptographic weakness, but from mispriced economic atomicity and cost-free identity or governance acquisition. ACM provides a rationality threshold theorem formalizing when attacks become economically and physically irrational. The model directly informs secure system architecture design by enforcing multi-layer cost escalation across cryptographic, physical, behavioral, and governance layers. This work is intended for cryptography, blockchain security, adversarial machine learning, economic attack modeling, and post-quantum system design.

Liuyu Yang, Xinxuan Zhang, Yi Deng, Zhuo Wu · 5 authors

No abstract is available for this record.

Roe, Paul

Classical ledger systems, including proof-of-work (PoW) and proof-of-stake (PoS) blockchains;derive their security from the assumption that irreversible computation incurs a thermodynamic cost. This assumption, rooted in Landauer’s principle, implies that reversing orre-writing global state requires expenditure of significant physical energy, and therefore canbe made economically infeasible.In this paper, we introduce the RHEA–Λ Gate Family: a reversible multi–radix (2–3–5)logic primitive with a triangular, measure-preserving topology that embeds directly intoHamiltonian phase-space flows. Each gate includes an intrinsic symbolic (glyph/entropy)register enabling perfect, lossless history retention without information erasure. Whencomposed into circuits, Λ–gates form fully reversible, entropy-preserving state-transitionoperators capable of implementing arbitrary classical computations at asymptotically zeroenergy in adiabatic regimes.We show that any ledger whose security relies on computational irreversibility becomesvulnerable in a computational substrate that supports (i) strictly reversible evolution, (ii)zero–entropy symbolic memory, and (iii) multi-radix reversible hashing. In such substrates,the economic barrier that protects ledger history vanishes: all PoW functions become ther-modynamically free, PoS penalties become reversible, and Merkle-tree hashing no longerprovides unidirectional security. We formalize this result as an impossibility theorem forirreversible-cost security models, and we construct a reversible ledger architecture whose cor-rectness is maintained through Hamiltonian invariants rather than dissipative computationalcost.The Λ framework thereby provides both (a) a constructive alternative to irreversible ledgermechanisms and (b) the first proof that classical reversible computation, when extendedto higher radices with symbolic memory, nullifies the energy-based assumptions underlyingmodern blockchain security.

Geoffroy Couteau, Srinivas Devadas, Alexander Koch, Sacha Servan-Schreiber

No abstract is available for this record.

Sana Ullah, Syed Muslim Jameel, Meghann Drury-Grogan, Mara Sintejdeanu · 5 authors

The complexity of cross-border regulatory compliance in the MedTech sector imposes significant administrative and financial burdens on manufacturers, characterized by manual processes, data redundancy, and country-specific, cross-border heterogeneous regulations. To address this, we present EireLedger, a decentralized framework that automates and cryptographically enforces regulatory compliance verification. EireLedger utilizes a novel dual-purpose zero-knowledge proof (ZKP) scheme, instantiated with Groth16 zk-SNARKs, which allows a manufacturer to prove a device dossier's compliance to a jurisdiction-specific regulator in a privacy-preserving manner, while simultaneously generating a verifiable ZKP-based access grant for the regulator. This cryptographic proof is immutably anchored to a permissioned Hyperledger Fabric blockchain, which orchestrates the protocol and maintains a minimal, auditable record. The corresponding encrypted dossier artefacts are stored off-chain in a private IPFS cluster. Our comprehensive evaluation demonstrates that on-chain proof verification is highly efficient with a median latency of 12.3 ms, and our integrated ZKP-as-access-control model reduces end-to-end audit latency by 40% compared to traditional attribute-based access control (ABAC) by eliminating external authorization calls. The on-chain storage footprint is constant at ~2.1 KB per audit, ensuring data minimization. The framework also supports right to erasure in compliance with GDPR, cryptographically unpinning a 5 GB dossier in under 90 s. These results establish EireLedger as a novel, privacy-preserving, and practical solution for cross-border regulatory compliance in the MedTech supply chains.

Rahees Ur Rehman, Gurjit Singh Bhathal

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Jasmine Cathrine Mathew

NFT (Non-Fungible Token) has emerged as a trending topic in the digital world. This article focuses on the working principle of NFTs and their practical applications in real-world scenarios. Ethereum blockchain serves as the foundational technology that powers NFTs. This document provides a comprehensive technical overview of Ethereum blockchain technology applied in the textile industry for maintaining product ownership verification and authenticity

Zi Yi Lim, Chien-Chia Huang, Yi-Da Chiang

This study presents a novel decentralized and secure Automatic Optical Inspection (AOI) framework utilizing blockchain technology and smart contracts deployed on Nvidia Jetson edge computing devices to address integration complexities, security vulnerabilities, and excessive energy consumption in traditional AOI systems. The proposed architecture leverages Jetson devices as both processing units for image analysis and blockchain nodes, creating a decentralized network where inspection results are recorded through smart contracts. This approach ensures data immutability, transparency, auditability, and system resilience through decentralization. Experimental evaluation across three Jetson device models (Nano, Xavier NX, and Orin Nano Super) demonstrate that blockchain operations consume minimal resources. With the newer hardware Jetson Orin Nano Super showing average CPU usage of only 5.35% during blockchain operations (including ordering service), significant computational capacity remains available for AI-driven image inspection tasks. This enables the simultaneous execution of computer vision and AI recognition, as well as secure blockchain data recording, on a single edge device. The research implementing blockchain technology in resource-constrained edge devices proves the feasibility of blockchain-secured AOI in manufacturing environments.

Yiteng Peng, Dongwei Xiao, Zhibo Liu, Zhenlan Ji · 7 authors

The increasing use of deep learning (DL) models has given rise to significant privacy concerns regarding training and inference data. To address these concerns, the community has increasingly adopted crypto-based privacy-enhancing technologies (CPET) like homomorphic encryption (HE), secure multi-party computation (MPC), and zero-knowledge proofs (ZKP). The integration of CPET with DL, often referred to as CPET-DL, is commonly facilitated by specialized frameworks like CrypTen, TenSEAL, and EZKL. These frameworks offer configurable parameters to balance model accuracy and computational efficiency during privacy-preserving operations. However, these configurations, while seemingly harmless, can introduce subtle vulnerabilities. The stealthy attacks induced by misconfigurations are hard to detect because 1) the plaintext models remain vulnerability-free, and 2) existing auditing tools are hardly applicable to CPET-hardened models. This creates a paradox: tools intended to protect privacy can be undermined through configuration manipulation.

Pierre Ghaly, Harald Gjermundrød, Ioanna Dionysiou

Blockchain tokenization ecosystems face significant challenges in complying with privacy regulations such as the General Data Protection Regulation (GDPR), particularly the “Right to Be Forgotten” mandate. The immutable nature of blockchain conflicts with the requirement for data deletion, creating a fundamental tension between technological capabilities and regulatory compliance. This paper presents a novel cryptographic audit framework for implementing GDPR-compliant data erasure in configurable tokenization systems. Our approach leverages cryptographic key destruction, zero-knowledge proofs for audit trails, and automated smart contract mechanisms to achieve practical data deletion while preserving blockchain immutability. The framework introduces a triple-layer architecture separating on-chain and off-chain references from off-chain sensitive data, enabling verifiable data erasure through cryptographic “shredding” techniques. We demonstrate the framework’s effectiveness through detailed algorithms and present a proof of concept comprehensive audit mechanism that generates cryptographic proofs of successful data deletion without revealing sensitive information. Our solution addresses critical gaps in current blockchain privacy implementations and provides a practical pathway for regulatory compliance in tokenization ecosystems.

Jinan Jiang, Haoran Qin, Xiapu Luo

Zero-knowledge proof (ZKP) circuits implemented in programming languages like Circom are fundamental to blockchain and privacy-preserving applications. These code often suffer from constraint-related issues where constraints fail to accurately specify intended computations. While existing analysis tools have been proposed, they struggle with large-scale circuits containing complex template embeddings. We present ScaleCirc, a novel framework that addresses such limitations through: 1) systematic management of analysis redundancy via circuit deduplication strategies; 2) constrainedness propagation methods leveraging source code semantic information; and 3) a generalizable framework for different circuit analysis tasks. Evaluation on 691 real-world circuits shows ScaleCirc demonstrates higher efficiency, and successfully analyzes many Circom programs that existing works failed on.

Bing Zhang, Guijuan Wang, Zhongyuan Yu, Anming Dong · 5 authors

Cross-chain payment, serving as critical infrastructure for multi-chain ecosystem interoperability, confronts the fundamental challenge of simultaneously ensuring privacy preservation, regulatory compliance, and quantum-resistant security—objectives that are inherently difficult to reconcile. This paper proposes a Lattice-based Dynamic Privacy-preserving Cross-chain Payment Scheme (LDPCPS) that innovatively integrates advanced cryptographic primitives. Specifically, LDPCPS employs a privacy-preserving scalar product (PPSP) protocol enabling ciphertext-domain aggregation and verification, constructs a dynamic regulatory framework using signatures of knowledge (SoK) for zero-knowledge compliance proofs and risk-triggered traceability, and implements proxy re-encryption to facilitate seamless quantum-resistant key migration. Experimental results demonstrate that LDPCPS has significant superiority over state-of-the-art alternatives in quantum resistance, computational efficiency, and regulatory adaptability, thereby establishing a robust foundation for secure and compliant cross-chain transactions.

Muhammad Fadhil Abidin, Lulu Chaerani Munggaran

<p>Document forgery remains a pervasive problem across education, government, and trade sectors. This paper presents a blockchain-based digital document verification system built on the Internet Computer Protocol (ICP). The approach computes SHA‑256 hashes of documents and anchors them to ICP canister smart contracts, ensuring integrity and non-repudiation without storing document contents. The system manages a registry of approved verifiers so that only trusted institutions can enroll documents. In evaluation with 15 documents (85–3025 KB) and five repeated trials per document, the prototype achieved an average verification time of 1.54 s and an accuracy of 99%. Compared with Ethereum-based baselines in prior work, the ICP-based design avoids gas fees and reduces verification latency. The proposed architecture supports future integration of zero-knowledge proofs (ZKP) to validate authenticity while preserving privacy.</p>

Liviu Ionut Epure

The convergence of quantum physics and machine learning presents unprecedented opportunities for developing ultra-secure authentication systems. This comprehensive paper investigates the integration of quantum random number generators (QRNGs) with advanced machine learning architectures, including quantum neural networks (QNNs), long short-term memory (LSTM) networks, and hybrid quantumclassical models, to establish authentication mechanisms with information-theoretic security guarantees. We provide rigorous theoretical foundations spanning quantum entropy theory, min-entropy estimation, and randomness certification, complemented by detailed analyses of contemporary QRNG hardware implementations including photonic integrated circuits achieving generation rates exceeding 20 Gbps. The paper explores deep learning architectures for biometric authentication, demonstrating how QNN-enhanced systems achieve superior performance through quantum superposition and entanglement. Furthermore, we examine the application of quantum entropy sources in zero-knowledge proof protocols, particularly zk-SNARKs and zk-STARKs, addressing post-quantum security concerns. Through comprehensive mathematical formulations, algorithmic implementations, and security analyses, we establish that hybrid quantum-classical authentication systems combining QRNG-derived cryptographic keys with ML-based behavioral authentication provide provably secure, practical solutions for next-generation cybersecurity applications. Experimental results from current quantum hardware platforms validate theoretical predictions and demonstrate real-world applicability.

Papp, László

Abstract — The Fractal Eavesdrop Detection (FED) protocol defines a cryptographic mutual-authentication... The Fractal Eavesdrop Detection (FED) protocol defines a cryptographic mutual-authentication and integrity validation mechanism between two fractal nodes sharing a recursive lineage. Unlike conventional systems that rely on fixed keys or static hashes, FED uses algorithmic mutability, session-based seed derivation, multi-point challenge validation, and time-bound CRC binding to detect both impersonation and passive eavesdropping. The protocol is designed for lightweight, low-power devices such as ESP32-class microcontrollers and operates without blockchain consensus or zero-knowledge proofs, while still enabling secure proof-of-origin and tamper-awareness. FED serves as the security and validation layer within the EQUORA Institute’s Fractal Economy architecture and complements the BlockFractal cryptographic tokenization layer and the EquoraVault hardware-based proof-of-impact system. This document is released as part of the EQUORA Institute White Paper Series and is a preprint version (v0.8), subject to revision. All versions remain archived for DOI-based citation integrity.

Yuanfeng Xie, Weiwei Jiang, Hanqing Luo, Jian Ping Gan

This study presents an innovative authentication scheme that integrates Physical Unclonable Functions (PUFs) and Zero-Knowledge Proofs (ZKP) to provide efficient and secure authentication for Internet of Things (IoT) devices. Traditional PUF-based protocols offer strong security but incur high resource costs and slow authentication. To address this, we propose a joint scheme. First, a unified architecture combining a PUF–True Random Number Generator (TRNG) is introduced. This architecture utilizes a feedback permutation obfuscation mechanism and an arbitration delay deviation with a metastable design from a ring oscillator, ensuring the PUF–TRNG system possesses both attack resistance and true random properties. The architecture provides synchronization for both PUF and TRNG in the protocol. Next, we integrate Schnorr’s ZKP with a PUF-based key encapsulation and reconstruction scheme to construct an end-to-end anonymous identity authentication protocol that does not require real-time participation of a trusted third party. The protocol requires only two handshakes, significantly reducing the number of protocol rounds compared to related protocols. Finally, the PUF–TRNG architecture has been implemented on the Xilinx XC7A100T development board. Experimental results show that the PUF circuit effectively resists various modeling attacks. Formal verification with ProVerif demonstrates confidentiality, mutual authentication, and robustness against mainstream attacks. The protocol reduces area overhead and computational time by 43.04% and 42.99%, respectively, compared to similar protocols.

Chaitanya Tumma, Supraja Ayyamgari, Rithwik Sannapu, Abhignan Srivatsava Sribhashyam · 5 authors

This research introduces a next-generation cryptographic framework aimed at securing payment systems and IoT-enabled financial ecosystems against both contemporary cyber threats and the anticipated risks posed by quantum computing. The proposed architecture supports intelligent payment cards, contactless transactions, and IoT-based banking infrastructures by integrating classical cryptographic methods—such as Advanced Encryption Standard (AES), Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC), Secure Hash Algorithm 3 (SHA-3), and Hash-based Message Authentication Code (HMAC)—with post-quantum algorithms, including CRYSTALS-Kyber and Dilithium. In addition, the framework aligns with widely adopted industry standards, such as the Payment Card Industry Data Security Standard (PCI DSS), Europay-MasterCard-Visa (EMV), ISO 27001, and National Institute of Standards and Technology (NIST) guidelines, thereby ensuring compliance and regulatory resilience. To address the evolving cyber threat landscape, the system integrates blockchain-based decentralized identity management with zero-knowledge proofs (ZKPs) for trustless authentication. Furthermore, privacy-preserving techniques such as Secure Multi-Party Computation (MPC) and Fully Homomorphic Encryption (FHE) are employed to enable secure processing of encrypted data. The security framework is further strengthened through AI-driven fraud detection, which leverages deep learning and federated learning models to detect anomalies in real time without compromising customer privacy. Additionally, confidential computing enclaves and hardware trust anchors—including Hardware Security Modules (HSMs), Trusted Platform Modules (TPMs), and Physically Unclonable Functions (PUFs)—are utilized to enhance system integrity and reliability. By combining quantum-resistant cryptography, privacy-preserving computation, and intelligent anomaly detection, this work presents an adaptive and future-proof security roadmap for the financial sector. The proposed framework is designed not only to counteract today’s sophisticated cyberattacks but also to maintain resilience against the emerging challenges of the quantum era.

Ankit Ravish, Yaron Hay, Manaswini Piduguralla, Rishabh Jain · 6 authors

A smart contract is a special type of transaction designed for the execution of automated logic on blockchains. Alas, smart contracts transactions are one of the major hindrances to blockchain throughput. Hence, improving the execution time of smart contracts is a prime challenge for Blockchains at large. To that end, concurrent execution of smart contract is an appealing direction, which has been adopted by several contemporary Blockchains like Solana, Aptos, Sui, Sei, and Monad. Executing smart contracts in parallel requires applying deterministic concurrency controls based on ensuring consistent ordering of all conflicting transactions in all miners/validators. Existing implementations rely on the Block's total ordering to resolve this requirement. Recently, it has been suggested that relying on minimal coloring of the conflict graph corresponding to the Block's transactions can provide a better performance potential, yet without any evaluation. In this paper, we compare between approaches to smart contracts parallelization. Our study’ finds that in many situations, indeed the coloring-based ordering leads to significantly better performance than the Block order preserving approach. However, this gain has its limits, and it is not always guaranteed. In particular, the results are largely dependent on the conflict ratio in the conflict graph and the type of application.

Archana Kurde, Sushil Kumar Singh, Manish Kumar, Krunal Vaghela · 5 authors

No abstract is available for this record.

Gyeombi Cheon, Yunmin Choi, Jiye Baek

No abstract is available for this record.

Mohini Verma, Mohona Ghosh, Amar Kumar Mohapatra

No abstract is available for this record.

Nikhil Sharma, Prashant Giridhar Shambharkar

No abstract is available for this record.

Devendra L. Bhuyar

The integration of Industrial Automation Systems (IAS) with the Internet of Things (IoT) under Industry 4.0 has significantly enhanced operational efficiency but also exposed critical communication infrastructures to cyber threats. Conventional security frameworks often fail to ensure end-to-end data integrity, authentication, and confidentiality in real-time industrial networks. This paper proposes a blockchain-enabled mathematical cryptography model designed to secure data transmission between industrial nodes. The framework utilizes Elliptic Curve Cryptography (ECC) for lightweight key generation, SHA-3 hashing for immutable transaction records, and smart contract-based consensus for autonomous trust management within a distributed ledger. A simulated industrial environment demonstrates that the proposed model achieves 42% faster encryption-decryption cycles and a 38% reduction in data latency compared to traditional asymmetric cryptosystems. The mathematical foundation ensures provable security under discrete logarithm assumptions, while blockchain consensus guarantees tamper resistance and auditability. This study contributes a scalable, mathematically robust architecture for secure data transmission in automation networks, offering potential integration within Supervisory Control and Data Acquisition (SCADA) and Programmable Logic Controller (PLC) environments.

Kesara Wimal, Gary Cullen, John Donovan

Blockchain and Physical Unclonable Functions (PUFs) are two transformative technologies that have gained significant attention in recent years. Blockchain is a distributed ledger technology that ensures secure, transparent, and tamper-proof transactions without the need for a central authority, making it indispensable across various industries. PUFs, on the other hand, leverage the inherent randomness in physical devices to generate unique identities for authentication and security, particularly in applications requiring low-cost, scalable solutions. The convergence of blockchain and PUFs promises to address critical security challenges, particularly in device authentication, data integrity, and privacy-preserving mechanisms. This research explores the synergy between blockchain and PUFs, aiming to identify commonalities in methodologies, key findings, limitations and future directions in existing literature. A total of 12 peerreviewed papers, each with over 10 citations, were selected based on their academic rigour and impact, representing the most influential studies published between 2019 and 2024. By employing thematic analysis, this research synthesises the past literature across three main objectives: methods, key findings, and both limitations and future work, identifying 8 distinct themes within these categories. The majority of the papers reviewed indicated a strong focus on authentication and access control within their methodological approaches. These studies frequently utilised enhanced cryptographic techniques and conducted security analyses as part of their frameworks. A common finding among the papers was their emphasis on security and efficiency, with many solutions being specifically applied to the Internet of Things (IoT) domain. However, there was a notable lack of comprehensive discussion regarding the limitations of the approaches. Furthermore, the papers often proposed that future research should address the development of novel consensus mechanisms. This study suggests that prospective research should focus on integrating smart contracts and further advancing scalable, decentralised solutions, extending beyond the IoT domain.