Blockchain Papers

Muhammad Ashraf Nazir, Nazim Hussain, Khalid Hamid, Muhammad Danish Rasheed, Muaaz Akhter, Muhammad Ibrar, Muhammad Aaqib Javed, Fareeha Zafar

No abstract is available for this record.

Goukassian, Lev

A systemic "evidentiary deficit" now characterizes automated global civicsystems, undermining regulatory oversight, institutional accountability, and public trust in high-stakes domains. The increasing opacity of high-speed, algorithmically-driven decisions in finance, public health, and environmental governance creates un-auditable risks. This report posits Ternary Logic (TL) as a neutral, non-ideological infrastructure framework designed to remediate this deficit. TL extends traditional binary logic by introducing a formal, third logical state: 0 (Epistemic Hold), distinct from 1 (Proceed) and -1 (Halt). This 0 state functions as a mandatory, auditable "computational hesitation" triggered by predefined uncertainty or risk thresholds. By instrumenting this pause, TL transforms deliberation and uncertainty from an operational failure into a cryptographically verifiable evidentiary asset. This report details the TL architecture through its Eight Pillars, which provide an integrated "accountability stack" mapping institutional policy to cryptographic proof. It describes the tri-cameral governance model—Technical Council, Stewardship Custodians, and Smart Contract Safeguard—architected for long-term resilience and prevention of institutional capture. Furthermore, it details the technical architecture, including a dual-lane, low-latency (<300ms) design, a hybrid-shield (public/private) ledger system, and a novel cryptographic stack (combining Ephemeral Key Rotation, Zero-Knowledge Proofs, and Cryptographic Erasure) that simultaneously satisfies regulatory demands for auditability, legal requirements for privacy (e.g., GDPR), and commercial protection of trade secrets. This framework provides a sovereign-grade blueprint for establishing provable accountability in systems governed by institutions such as the Bank for International Settlements (BIS), U.S. Securities and Exchange Commission (SEC), U.S. Food and Drug Administration (FDA), and World Health Organization (WHO).

Ervin Goci, Suzanne Harris-Brandts

Amidst a global surge in urban development foregrounding smart, sustainable, and now pandemic-proof design, new forms of longstanding hazard and health discourse are being deployed to justify dispossession, particularly in self-built districts. Looking at a case in Tirana, Albania, we examine how this discourse is being contemporized and paired with diverse digital media as it overlaps within a single project – the Tirana Riverside Project. As expressions of authoritative knowledge, architectural visualizations, social media posts, state-backed news broadcasts, and international design blogs work to validate this underlying ideology driving demolition. Such imagery and discourse, we contend, thus need to be read as artifacts of power that entrench government authority while substantiating mass dispossession. We argue that these processes are not only about the real estate profits demolition-based urban renewal generates but also – in Albania’s flawed democratic context – their ability to support the ruling elite. Using content analysis, we identify four hazard and health imaginaries overlapping within the project: 1) pandemic-proof design; 2) design for seismic emergencies; 3) zero-emission sustainable design; and 4) smart city technologies. We show how these imaginaries join longer traditions of normative discourse around hazard and health in urban renewal yet scrutinize their distinct contemporized and localized expressions.

Chalamalasetty Nishitha, Yelavarti Kalyan Chakravarti, V. Esther Jyothi

No abstract is available for this record.

Rana Alaa, Darío González-Ferreiro, Carlos Beis-Penedo, Manuel Fernández‐Veiga · 6 authors

Split learning is an approach to collaborative learning in which a deep neural network is divided into two parts: client-side and server-side at a cut layer. The client side executes its model using its raw input data and sends the intermediate activation to the server side. This configuration architecture is very useful for enabling collaborative training when data or resources are separated between devices. However, split learning lacks the ability to verify the correctness and honesty of the computations that are performed and exchanged between the parties. To this purpose, this paper proposes a verifiable split learning framework that integrates a zk-SNARK proof to ensure correctness and verifiability. The zk-SNARK proof and verification are generated for both sides in forward propagation and backward propagation on the server side, guaranteeing verifiability on both sides. The verifiable split learning architecture is compared to a blockchain-enabled system for the same deep learning network, one that records updates but without generating the zero-knowledge proof. From the comparison, it can be deduced that applying the zk-SNARK test achieves verifiability and correctness, while blockchains are lightweight but unverifiable.

Ridwan Abdulsalam

The increasing complexity and sophistication of financial fraud have necessitated more effective and real-time solutions for monitoring, detecting, and preventing illicit activities in the financial sector. Blockchain technology, with its inherent features of decentralization, immutability, and transparency, has emerged as a promising tool to address these challenges, particularly when integrated with Regulatory Technology (RegTech) systems. This explores the potential of blockchain-powered RegTech solutions for enhancing fraud detection and supporting legal oversight in financial institutions. Blockchain’s decentralized ledger system provides a secure and transparent environment where financial transactions can be monitored in real time. The integration of machine learning algorithms with blockchain analytics allows for the identification of suspicious patterns and anomalies, enabling rapid detection of fraudulent activities. Additionally, blockchain facilitates the automation of compliance reporting, reducing operational costs and ensuring regulatory standards are met with minimal human intervention. The use of smart contracts further streamlines the enforcement of compliance rules, providing a seamless and tamper-proof audit trail. Furthermore, blockchain has the potential to harmonize international compliance standards, enabling more efficient cross-border regulatory enforcement. Through its use in decentralized identity verification and AML (Anti-Money Laundering) systems, blockchain can enhance the traceability and transparency of financial transactions, addressing the challenges of jurisdictional fragmentation and inconsistent regulations across countries. Privacy-preserving technologies, such as zero-knowledge proofs, also ensure that data protection laws like GDPR are respected while maintaining regulatory oversight. This concludes by highlighting the substantial benefits blockchain-powered RegTech systems offer for real-time fraud detection and regulatory compliance, urging financial institutions and regulators to collaborate on adopting these technologies to safeguard the integrity of global financial systems. Keywords: Harnessing, Blockchain-powered, RegTech systems, Real-Time, Fraud Detection, Legal Oversight, Financial Institutions.

Runzhong Yu, Wu Yang, Liyuan Zhang

ABSTRACT To address the core challenges of information asymmetry, privacy leakage, and low storage efficiency in rice supply chains, this study proposes an enhanced traceability system that integrates blockchain, adaptive encryption, and lightweight zero‐knowledge proofs. The system features a dynamic role‐based encryption model, where encryption levels are determined by both data sensitivity and role‐based weights. This model was designed and validated through surveys involving 50 stakeholders. By adopting an on‐chain and off‐chain collaborative storage architecture that leverages Merkle trees and IPFS, the system achieves a 67% reduction in storage overhead. Furthermore, an optimized Groth16‐based ZKP protocol ensures rapid verification in under 180 ms on ARM‐based devices. Experimental results demonstrate that, at a scale of 100,000 records, the system attains a transaction processing capacity of 328 TPS and an information entropy of 3.87, representing a 51% improvement over single‐layer encryption schemes. The monthly deployment cost remains affordable for smallholder farmers, ranging from $2 to $5. The system also supports interoperability with external traceability frameworks through cross‐chain channels and adaptation to the GS1 EPCIS standard, facilitating trusted collaboration in transnational rice supply chains. By effectively balancing data integrity and privacy protection, this solution significantly enhances system scalability and offers a novel pathway for the digital transformation of agricultural supply chains.

John Adeyemi O, Folasade Yetunde Ayankoya, Kuyoro S. O

The advancement of technology has positioned blockchain and machine learning (ML) as transformative forces in finance. Blockchain’s decentralized structure ensures secure and transparent transactions, while ML processes vast data to identify patterns and enhance decision-making. Their integration offers significant potential for fraud detection, risk assessment, and transaction optimization. Blockchain provides a tamper-proof environment, ensuring data integrity and reducing fraud. Meanwhile, ML detects anomalies, predicts market trends, and automates processes, improving financial security and efficiency. However, challenges such as scalability, computational demands, and data privacy hinder widespread adoption. Blockchain struggles with high costs and limited throughput, while ML requires significant resources and quality data. Emerging solutions like federated learning for privacy-preserving ML, zero-knowledge proofs for secure transactions, and hybrid blockchain models for scalability aim to address these challenges. Overcoming these barriers will enable a more secure, efficient, and data-driven financial ecosystem.

Korn Dhampiban-udom, Natchanan Koson, Chonnawee Udompongpipat, Somchart Fugkeaw

As cloud adoption grows, Identity and Access Management (IAM) faces increasing complexity due to reliance on centralized systems controlled by Cloud Service Providers (CSPs), raising concerns over data leakage and single points of failure. This paper proposes BZET-IAM, a trust-based Single Sign-On IAM (SSO-IAM) framework built on Consortium Blockchain to support hybrid, multi-application cloud environments across domains. The system integrates Zero Trust Architecture (ZTA), Self-Sovereign Identity (SSI), and Zero-Knowledge Proofs (ZKPs) to enable dynamic, privacy-preserving authentication using verifiable claims. Trust-scores and contextual information are embedded in access tokens to support continuous verification and adaptive access control. Blockchain ensures tamper-proof, auditable authentication and authorization. Experimental results show that the proposed approach achieves lower authentication and privilege update costs under dynamic, context-aware access scenarios.

Vítor Miguel Vieira Neves, António Grilo, Mário Monteiro Marques

Many frameworks have emerged as a valuable communication protocol for lightweight, interoperable data exchange between distributedplatforms and control stations. However, some of these frameworks was that a broker-centric architecture poses significant risks when applied to military-grade deployments where reliability, security, and operational continuity are non-negotiable. This study analyses the vulnerabilities of frameworks with a single point off ailure, such as those based on Message Queuing Telemetry Transport(MQTT) in military contexts, and proposes a decentralised architecture that integrates hierarchical blockchain layers secured by zero-knowledge Scalable Transparent Arguments of Knowl edge(zk-STARK)proofs. The approach aims to enhance survivability, auditability, and trust in UxV communication networks, offering a foundation for next-generation secures warm coordination across multi-domain operations.

Vladlen D. Afonin, Sergey Zapechnikov

В статье рассматривается проблема применимости отечественных криптографических алгоритмов в современных системах доказательств с нулевым разглашением (zero-knowledge proofs, ZKP), которые находят широкое применение в блокчейнах, цифровой идентификации, медицинских и биометрических системах, а также в задачах машинного обучения и защиты конфиденциальных данных. Особое внимание уделено использованию алгоритма хэширования ГОСТ 34.11-2018 «Стрибог» в качестве случайного оракула в протоколах доказательств, где этот компонент играет ключевую роль для обеспечения корректности и безопасности вычислений. В работе проводится краткий обзор современных парадигм построения систем доказательства выполнения произвольных вычислений. В качестве объекта анализа выбран постквантовый протокол FRI, который активно применяется в агрегируемых ZKP-системах и опирается на использование хэш-функций для построения деревьев Меркля и генерации случайных элементов поля. Авторами реализована экспериментальная версия протокола FRI на языке Python с возможностью замены криптографических примитивов и проведения замеров производительности. В рамках эксперимента стандартные хэш-функции семейства Keccak были заменены на «Стрибог», что позволило провести сравнение времени работы протокола. Результаты показали, что использование ГОСТ 34.11-2018 приводит к замедлению генерации доказательства примерно в два раза, однако такое снижение производительности не является критичным для приложений, где ключевым фактором выступает соответствие национальным стандартам и регуляторным требованиям. Сделан вывод о принципиальной возможности применения алгоритма «Стрибог» в ZKP-протоколах и обозначены направления дальнейшей оптимизации его использования, включая аппаратные ускорения и адаптацию к современным моделям построения хэш-губок.

Festus Okechukwu Ogbunude, Augustine Chidiebere Onuora, Daberechi David Ekuma, C.E. Madubuike · 5 authors

Persistent electoral irregularities—ranging from vote manipulation and ballot stuffing to logistical failures and post-election violence—continue to undermine democratic consolidation across developing democracies. Nigeria, Africa’s largest democracy, epitomizes this crisis, where recurrent allegations of fraud, digital failures, and institutional mistrust have eroded public confidence in electoral outcomes. This paper proposes a secure, transparent, and technically robust blockchain-based electoral framework tailored for developing democracies. Leveraging the core attributes of blockchain—immutability, decentralization, real-time auditability, and cryptographic security—we design a technical architecture for voter registration, ballot casting, vote tallying, and public verification. The system integrates smart contracts, Proof of Authority (PoA) consensus, cryptographic identity verification, and zero-knowledge proofs to ensure integrity, privacy, and resilience. We analyze implementation challenges including the digital divide, cybersecurity threats, and legal gaps, and propose a phased, stakeholder-driven roadmap anchored in Nigeria’s institutional context. Comparative insights from Estonia, Sierra Leone, and Brazil underscore the importance of local ownership, institutional autonomy, and civic literacy. The paper contributes a practical, context-sensitive blueprint for blockchain-based electoral reform, bridging the gap between theoretical innovation and real-world deployment in fragile democratic ecosystems.

Jiancheng Zhao, Chunhui Zhao, Jiaqi Yue

No abstract is available for this record.

Rashi Saxena, Pratibha Kumari

The increasing reliance on smart grids to manage power distribution efficiently has introduced significant cybersecurity vulnerabilities due to their interconnected nature. Traditional security approaches often fall short in real-time protection, particularly against advanced threats such as data manipulation, unauthorized access, and Distributed Denial-of-Service (DDoS) attacks. This paper proposes a novel Smart Grid Secure Protocol (SGSP), integrating Attribute-Based Zero-Knowledge Proofs (AB-ZKP), Redundant Consensus Mechanisms combining Proof of Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT) for scalable and fault-tolerant consensus, and Grid Safe Smart Contracts (GSSC) to enhance data confidentiality, automate security enforcement, and resist cyber threats. The AB-ZKP mechanism ensures selective attribute verification while preserving privacy and keeping sensitive data off-chain. The hybrid consensus mechanism merges energy-efficient PoS with fault-tolerant PBFT, securing the Blockchain layer against DDoS and Sybil attacks. Meanwhile, GSSCs automate transaction validation and policy enforcement, reducing human intervention and enabling real-time anomaly detection. Experimental results in a simulated environment demonstrate high resilience, improved data privacy (98.7% compliance), fast consensus (1.2 s), low energy consumption (0.09 kWh/transaction), and strong DDoS resistance (92.5/100). The proposed approach significantly outperforms traditional methods, paving the way for secure, scalable, and privacy-preserving smart grid ecosystems.

V.Ananya Sree, Aparna Tanam

No abstract is available for this record.

Tara Swaraj, Krishanu Nath, Manas Kumar Bera, Rajiv Kumar Mishra · 6 authors

This paper addresses the design of distributed adaptive control protocols for leader-follower consensus and time-varying formation problems, where agents communicate over directed graphs. Projection operator-based adaptive control protocols are developed for multi-agent systems modelled as general uncertain linear dynamics. An integral sliding mode-based robust control strategy is developed to compensate for the unknown bounded disturbance in the followers' dynamics. To relax the knowledge of the upper bound of the disturbance in designing a sliding-mode controller, a barrier function-based adaptive integral sliding-mode controller is designed to adjust the gain of the discontinuous part of the controller. This technique avoids overestimation of gains, which significantly reduces chattering. This control technique ensures the convergence of disagreement variables in a predefined neighborhood of zero. The Lyapunov-based stability proof demonstrates the convergence of disagreement variables in leader-follower consensus and time-varying formation control problems. Finally, numerical examples are provided to validate the efficacy of the proposed protocols.

Yan Gérard, Pascal Lafourcade, Lola-Baie Mallordy, Léo Robert

No abstract is available for this record.

Dr. Emily R. Thompson

Blockchain technology has emerged as a transformative solution for decentralized and secure data management. However, the security of blockchain networks heavily relies on robust cryptographic protocols. This article provides a comprehensive analysis of key cryptographic techniques employed in blockchain security, including hash functions, digital signatures, consensus algorithms, and zero-knowledge proofs. We evaluate their roles in ensuring data integrity, authentication, confidentiality, and resistance to common attacks such as double-spending and Sybil attacks. A comparative study highlights the strengths and limitations of these protocols, guiding future enhancements for blockchain security frameworks

Poornima G. Naik, Rajani S. Kamath, S. S. Jamsandekar

Blockchain technology has gained prominence in academia as a promising solution to strengthen security, transparency, and interoperability in academic assessment systems. The current research presents the design and implementation of a decentralized Ethereum based examination framework using Ethereum smart contracts, role-based access control (RBAC), and ERC20-based fungible tokenization for academic credits. In contrast to prior works which focus primarily on conceptual frameworks, the proposed system is deployed and evaluated on the Ethereum and Polygon (Layer2) test networks. Further, the architecture integrates off-chain storage via IPFS to minimize gas consumption, while role-specific smart contracts ensure data privacy and regulatory compliance. To enhance trust and confidentiality, zero-knowledge proofs (ZKPs) are incorporated for privacy-preserving answer verification, and decentralized identity (DID) standards are applied for student authentication. Experimental results demonstrate that Layer-2 deployment reduces transaction costs by up to 65% compared with Ethereum mainnet, and the system achieves stable throughput of 42 transactions per second (TPS) under simulated student workloads. Security validation using Slither and MythX confirms resistance to re-entrancy, double-spending, and unauthorized access attacks. The proposed framework establishes a quantifiable, fraud-resistant, and efficient academic assessment model, paving the way for scalable blockchain adoption in higher education.

Filippo Scaramuzza, Renato Cordeiro Ferreira, Giovanni Quattrocchi, Damian Andrew Tamburri · 5 authors

Classical software verification and validation techniques, such as procedural audits, formal methods, or model documentation, are the traditional mechanisms used to achieve the verifiable accountability now required by regulations like the EU AI Act. These methods are either expensive or heavily manual, and ill-suited for the opaque, "black box" nature of most Artificial Intelligence (AI) models. A conflict arises: high auditability and verifiability are required by law, but such transparency conflicts with the need to protect the assets being audited (e.g., confidential data and proprietary models). This paper introduces ZKMLOps, an \ac{MLOps} verification framework that operationalizes Zero-Knowledge Proofs (ZKPs) within Machine-Learning Operations lifecycles; a ZKP allows a prover to convince a verifier that a statement is true without revealing any information about the statement itself. By integrating ZKP with established software engineering patterns, ZKMLOps provides a modular and repeatable process for generating verifiable cryptographic evidence-proofs of well-defined computational statements about the audited model and its inputs-that auditors can use as input to a regulatory compliance determination. We evaluate the framework along two dimensions. First, framework viability: orchestration overhead is bounded and stable across architecturally heterogeneous ZKP backends and models of increasing size. Second, cost-versus-assurance trade-offs: the audit-on-demand setting is the regime in which full zero-knowledge auditing is the appropriate tool, where it provides confidentiality and integrity guarantees that lighter-weight alternatives cannot match.

Haohua Duan, Liyao Xiang, Xin Zhang

Watermarking schemes for large language models (LLMs) have been proposed to identify the source of the generated text, mitigating the potential threats emerged from model theft. However, current watermarking solutions hardly resolve the trust issue: the non-public watermark detection cannot prove itself faithfully conducting the detection. We observe that it is attributed to the secret key mostly used in the watermark detection -- it cannot be public, or the adversary may launch removal attacks provided the key; nor can it be private, or the watermarking detection is opaque to the public. To resolve the dilemma, we propose PVMark, a plugin based on zero-knowledge proof (ZKP), enabling the watermark detection process to be publicly verifiable by third parties without disclosing any secret key. PVMark hinges upon the proof of `correct execution' of watermark detection on which a set of ZKP constraints are built, including mapping, random number generation, comparison, and summation. We implement multiple variants of PVMark in Python, Rust and Circom, covering combinations of three watermarking schemes, three hash functions, and four ZKP protocols, to show our approach effectively works under a variety of circumstances. By experimental results, PVMark efficiently enables public verifiability on the state-of-the-art LLM watermarking schemes yet without compromising the watermarking performance, promising to be deployed in practice.

Ren Shengchuang, Jiping Xu, Liu Chenze, Chen Mingyang · 5 authors

Short-shelf-life foods have received increasing attention in daily dietary practices due to their freshness and convenience. Compared with other food categories, they impose more stringent requirements on quality assurance and end-to-end traceability. To address the challenge of balancing timeliness in high-frequency real-time data collection with the authenticity of trusted information flow in short-shelf-life food supply chains, this paper proposes a trusted information collection mechanism based on lightweight blockchain technology. First, a multi-layer collaborative architecture is designed, encompassing the perception layer, edge gateway, blockchain layer, and application layer. By integrating off-chain storage with on-chain indexing, the mechanism effectively alleviates the storage and computational burden on the main blockchain. Second, the edge gateway layer incorporates a zero-knowledge interval proof module, enabling real-time, localized privacy compliance statements for sensitive data. Meanwhile, the blockchain layer adopts an PoA consensus mechanism, whereby authorized nodes perform rapid data verification and deposition. Finally, through theoretical analysis and simulation-based validation, the results demonstrate that the proposed mechanism not only enhances the real-time performance, authenticity, and privacy protection of data in short-shelf-life food supply chains, but also achieves high operational efficiency and scalability. This work thus provides a novel theoretical foundation and practical approach for trusted information collection in the context of short-shelf-life food supply chains.

Iflah Aijaz, Roohie Naaz Mir

Introduction: Ensuring secure and efficient identity management is crucial in an era where digital identities support numerous online services. Traditional Identity Management Systems (IDMS) face challenges such as data breaches, lack of user autonomy, and centralization risks, necessitating the exploration of decentralized alternatives. The review paper assesses blockchain-based IDMS as a potential solution, examining its benefits, challenges, and the role in improving security and privacy. Methods: This study conducts a systematic literature review of recent advancements in blockchain- based IDMS, drawing from peer-reviewed sources published between 2017 and 2024. The analysis focuses on security mechanisms, integration challenges, technological innovations, and their implications for digital identity management. Results: Blockchain-based IDMS offer many significant advantages, like enhanced security and privacy protection of identity data by the users. However, many challenges remain ahead, including the issue of scalability, interoperability with existing systems, and issues of regulatory acceptance. The advancements in this space are driven forward by recent innovations, such as zero-knowledge proofs and decentralized identifiers. Discussion: The review highlights blockchain’s transformative potential in addressing the flaws of traditional IDMS. While it offers notable improvements in privacy and user autonomy, successful real-world deployment requires overcoming technical and legal hurdles. These include adapting to existing standards and gaining acceptance among regulatory bodies. Conclusion: Blockchain technology has the potential to revolutionize digital identity management by overcoming several traditional IDMS limitations. While promising, further research is needed to overcome integration challenges and ensure regulatory compliance for real-world deployment.

Ping Wang, Fei Tang, Ankui Jing, Lei Liu · 6 authors

In vehicular ad-hoc networks (VANET), federated learning enables vehicles to collaboratively train global models for intelligent transportation without sharing raw data. However, global model training faces various potential risks, such as identity leakage, privacy inference, and malicious attacks, due to the dynamic network structure and untrusted wireless communication of VANET. To address these issues, a robust authentication mechanism for federated learning must be achieved to ensure the trustworthiness of model parameters. In this paper, we propose an efficient and privacy-preserving authentication scheme with lightweight encryption for federated learning in VANET using self-sovereign identity (SSI), called Fed-EALE. Fed-EALE constructs Merkle pseudonym identity trees with the aid of decentralized identifiers. Vehicle participants use unlinkable pseudonyms to achieve privacy protection. Fed-EALE utilizes verifiable credentials and zero-knowledge proof to build the authentication protocol to ensure the authenticity and integrity of model parameters from anonymous vehicles. In addition, to accurately identify and eliminate malicious participants in anonymous communications, Fed-EALE can track and recover the real identities of malicious vehicles. We perform a security analysis of Fed-EALE. Performance evaluations indicate that Fed-EALE reduces authentication overhead by approximately 76% compared to state-of-the-art protocols, while maintaining high stability and scalability in VANET.