Blockchain Papers

Ankit Sitaula, Ashraf Uddin, John Ayoade, Nam H. Chu · 5 authors

Counterfeit and unsafe medicines pose significant risks to patient safety and undermine trust in healthcare systems. This paper presents ACTMeds, a blockchain-supported pharmaceutical traceability and recall platform that considers pharmaceutical supply chain requirements and public health operational needs relevant to the Australian Capital Territory (ACT). The system integrates Ethereum smart contracts, developed using Ganache, with a React-based web application providing regulator, operator, pharmacy, and auditor interfaces, alongside a public verification portal leveraging QR and GS1 barcodes. In addition, role-based access control is enforced across the medicine lifecycle, including manufacture, custody transfer, dispensing, and recall, with immutable on-chain events generated to support auditability and accountability. To balance transparency with confidentiality, the platform prototypes a zero-knowledge (ZK) recall mechanism in which regulators can cryptographically prove that recall conditions meet predefined policy requirements without disclosing sensitive incident details. Threat modeling was conducted using the STRIDE framework, and security evaluation combined static application security testing (Solhint and ESLint) and dynamic testing. The paper further discusses deployment options, cost considerations, ZK recall performance analysis, ethical implications, and future enhancements. Security testing validated the platform’s resilience, with no high-severity vulnerabilities identified and medium-severity issues related to HTTP security headers addressed. The results indicate that a regulator-led, privacy-preserving, tamper-evident ledger can improve medicine authenticity verification and recall responsiveness while maintaining compliance and data protection obligations.

George Sebastian, Neethu Tom, Saritha M S, Vimal Babu P

Existing cloud storage auditing mechanisms rely on third-party auditors (TPAs) or centralized verification, introducing single points of failure and trust assumptions. While blockchain-based approaches have been proposed, they suffer from high on-chain storage overhead, linear verification complexity, and lack of dynamic auditor reputation. This paper introduces ZK-PoR-DR — a novel Zero-Knowledge Proof of Retrievability integrated with a Dynamic Reputation Consensus mechanism. Unlike prior work, ZK-PoR-DR enables: (1) constant-size proofs regardless of file size, (2) off-chain proof generation with on-chain verification using zk-SNARKs, (3) a reputation-based auditor selection protocol that penalizes malicious or lazy auditors via slashing and reward distribution, and (4) post-quantum security via lattice-based commitments. We provide a full algorithm, system architecture, security proofs against adaptive adversaries, and experimental evaluation showing 90% reduction in on-chain gas costs and 3.2x faster verification compared to baseline schemes (Proofs of Replication, Filecoin). No prior work has combined these four properties simultaneously. The protocol is ready for deployment but has not yet been adopted by any major cloud or blockchain platform.

Syed Abrar Ahmed, Ricardo Correia Bezerra, Simon Lewerenz, Henrique Martins

The EHDS Regulation establishes patient opt-out rights for data use, yet current implementations face fragmented registries and limited tamper-proof mechanisms. In this context, opt-out refers to a patient's proactive right to object to the reuse of their health data for purposes beyond direct clinical care. We propose a distributed ledger technology (DLT)-based architecture to enhance opt-out management. Using design research and regulatory analysis of EHDS and TEHDAS, we developed a proof-of-concept leveraging permissioned DLT, smart contracts, and decentralised identifiers for an immutable registry. This architecture aligns with EHDS requirements for tamper-evident audit trails and cross-border verification. This work bridges regulatory mandates with patient-centric governance across the EU.

David Rousell, Mallika Robinson, Nerkez Opacin, Maud Cassaignau · 9 authors

The shared mission of this HIPI-2 Initiative is to incentivise mass participation and financing of urban forest regeneration in Melbourne and across Victoria. To achieve this, the initiative built and tested diversified methodologies, platforms, and systems of valuing urban forests which factor in a far greater range of qualitative and quantitative indicators than existing models which separate ‘natural’ and ‘social’ benefits. These include new models of decentralised investment and exchange through geo-locative and Web3 technologies, opening novel opportunities for diversified systems of value creation through and for Victoria’s urban forests. The initiative investigates how emerging technologies offer multiple possibilities for large-scale impact, including:· connecting Victorians to the diverse benefits of urban forests· incentivising Victorians to actively participate in urban regeneration and knowledge sharing· revitalising diverse cultural and historical relationships with urban forests· enabling new legal and economic status for Victoria’s urban forests· building transdisciplinary literacies and lifelong learning through Victoria’s urban forestsTo realise these possibilities, the initiative has established strong research and impact partnerships to investigate the benefits and capabilities of emerging eco-digital platforms to scale up urban greening across Victoria. This initiative bridges leading RMIT research on urban greening, digital economies, regenerative education, and social innovation to develop new pipelines for regenerating and revaluing urban forests at scale.

Farman Guliyev

Abstract. The BeTrueCore protocol is presented — a decentralized collective decision-making system designed to resolve the fundamental contradiction between the authenticity of collective expression and manipulative influence in the digital environment. The system integrates three innovations: The Vote Weight Unit (VWU) model, which quantifies individual contributions based on time-weighted quality metrics rather than financial or institutional status. A cryptographic architecture based on the Minimal Anti-Collusion Infrastructure (MACI) and Zero-Knowledge Proofs (ZK-Proofs), decoupling the act of observation from its social consequences. An AI governance layer restricted to a "read-only" mode, delegating the final decision exclusively to cryptographic verification. Within the VWU model, the cumulative rating evolves via exponential smoothing with adaptive learning correction. The concept of the "Panopticon-Stent" is introduced as a novel architectural principle for transforming surveillance infrastructure from an instrument of control into an instrument of collective self-knowledge. We further propose the 23×32 ethical coding framework, mapping 23 Asilomar AI principles against 32 Thoughtful Decision Seeds Hygiene parameters to generate 736 technical ethical requirements, constituting a "digital DNA" for AI systems serving humanity. Keywords: collective decision-making, zero-knowledge proofs, MACI, vote weight unit, AI governance, digital democracy, Panopticon, Wabi-sabi, ethical coding, Web3

Diana-Elena Niți, Liviu Miclea, Szilárd Enyedi

No abstract is available for this record.

Craig S Wright

This online appendix accompanies the main paper of the same title. It contains thefull proofs of the propositions stated in the main paper, the multi-regime Jacobian andbifurcation analysis, the notation table, and the code-and-data documentation for theempirical execution. Section and equation references that appear in this document referto the main paper unless explicitly prefixed by OA-.

Wang Qingsheng, Runping Wu, su Zhanguo

Introduction, Sports science data governance is characterized by persistent tensions between data sharing, stakeholder incentives, and regulatory constraints. These challenges are amplified by fragmented data infrastructures and competing interests among stakeholders, limiting the effective use of data in performance optimization and research. Objective, This study aims to develop and theoretically ground a decentralized autonomous organization (DAO)-based governance framework for sports science data ecosystems, focusing on how decentralized mechanisms can enhance coordination, participation, and compliance. Methodology, A multi-method research design is employed, integrating conceptual case analysis, agent-based modeling (ABM), and survey-based empirical analysis. Structural equation modeling (SEM) is used to examine the relationships between governance perceptions, incentives, and data-sharing intentions. Results, The findings indicate that DAO-based mechanisms can support more distributed and transparent data-sharing processes. Simulation results suggest that participation dynamics follow non-linear patterns, with incentive and reputation mechanisms contributing to system stabilization. Empirical results identify technical usability, perceived regulatory compliance, and incentive structures as significant predictors of stakeholder participation. Discussion, The study contributes to platform governance and institutional theory by conceptualizing a hybrid decentralized governance model for data-intensive environments. The findings highlight the importance of aligning technological design with usability and regulatory requirements. However, limitations related to model assumptions, perception-based data, and interoperability challenges remain. Future research should focus on real-world implementation and the development of standardized governance frameworks.

Şafak Kayıkçı, Taghi M. Khoshgoftaar

Federated Learning (FL) enables collaborative model training across decentralized data silos without raw data exchange, making it particularly attractive for privacy-sensitive domains like financial fraud detection. However, FL introduces critical vulnerabilities, notably the poisoning of global models through malicious client updates. Traditional defense mechanisms often rely on computationally expensive aggregation rules or complex anomaly detection. This paper introduces PoSFedFraud, a robust framework that integrates a Proof-of-Stake (PoS) economic layer directly into the federated aggregation process for financial fraud detection. By combining staking mechanisms with a dynamic reputation system, PoS-FedFraud economically disincentivizes adversarial behavior through automatic slashing and trust decay. We simulate a toy fraud detection scenario using a 29-dimensional feature space, demonstrating how the framework defends against norm-based gradient poisoning attacks. Our experimental results show that PoS-FedFraud successfully identifies and penalizes malicious actors—reducing their stake and trust upon detection—while maintaining global model convergence. The proposed method offers an incentive-compatible punitive layer that complements existing robust aggregation and anomaly-detection techniques for decentralized financial applications.

В.В. Абрамов, Georgiy Kolesnik

Данная работа посвящена комплексному анализу эволюции моделей управления цифровыми платформами и организациями в контексте перехода от Web2 к Web3 и формирования перспективной парадигмы управления пост-Web3. Подробно рассмотрены основные существующие модели управления, выявлены их сильные и слабые стороны. Проведён многокритериальный сравнительный анализ трех моделей (централизованной, ДАО и гибридной) по параметрам демократичности, экономической эффективности и устойчивости к рискам. Результаты анализа демонстрируют, что ни одна из «чистых» моделей управления не является универсально оптимальной. При этом наиболее сбалансированные результаты по всем группам критериев демонстрирует гибридная модель, что позволяет считать создание гибридных институтов, обеспечивающих баланс между децентрализацией, эффективностью и устойчивостью к манипуляциям, основой для формирования пост-Web3 архитектуры управления. Излагаются принципы построения пост-Web3 системы управления как адаптивной, многоуровневой системы, сочетающей операционное ядро, экспертные суб-ДАО и широкое сообщество. Ключевыми характеристиками пост-Web3 являются переход от количественного голосования токенами к управлению на основе репутации и вклада участников (меритократии), внедрение алгоритмической легитимности и интеграция искусственного интеллекта для поддержки принятия решений. This paper is devoted to a comprehensive analysis of the evolution of management models for digital platforms and organizations in the context of the transition from Web2 to Web3 and the formation of a future post-Web3 management paradigm. The main existing management models are considered in detail, their strengths and weaknesses are identified. A multi-criteria comparative analysis of three models (centralized, DAO, and hybrid) is carried out in terms of democracy, economic efficiency, and risk tolerance. The results of the analysis demonstrate that none of the pure management models is universally optimal. At the same time, the hybrid model demonstrates the most balanced results in all groups of criteria, which makes it possible to consider the creation of hybrid institutions that ensure a balance between decentralization, efficiency and resistance to manipulation as the basis for the formation of a post-Web3 management architecture. The principles of building a post-Web3 management system as an adaptive, multi-level system combining an operational core, expert subdomains and a broad community are outlined. The key characteristics of post-Web3 are the transition from quantitative token voting to reputation-based management and participant contributions (meritocracy), the introduction of algorithmic legitimacy, and the integration of AI to support decision-making.

Dongjae Shin, Minseon Park, Hyung‐Jin Yoon

This paper presents a Web3-based healthcare system integrated with the Republic of Korea's MyHealthWay platform for secure and user-controlled management of personal health data. The system combines decentralized identifiers, smart contracts, distributed storage, and the HL7 FHIR standard to support decentralized authentication, access control, and interoperability. A conceptual demonstrator, HealthCube, validates feasibility by enabling privacy-preserving health data processing through computation on encrypted data without exposing original information.

Kimon Antonios Provatas, Christos Galanopoulos, Ilias Georgakopoulos-Soares

Polygenic risk scores (PRSs) aggregate genetic effect estimates to predict disease susceptibility, yet clinical deployment often exposes raw genotype data to third-party compute infrastructure. Prior homomorphic-encryption approaches, still require trust in a designated evaluator. We present bioETH-PRS, a protocol that replaces that evaluator role with immutable smart contracts on a blockchain supporting Fully Homomorphic Encryption (fhEVM). Using the integer-exact TFHE scheme, bioETH-PRS computes the PRS dot product entirely within the encrypted domain, keeping both genotype dosage vectors and GWAS weight vectors hidden from external parties throughout execution. We introduce a three-step fixed-point quantisation scheme for representing signed GWAS weights as unsigned 64-bit integers, achieving machine-epsilon reconstruction accuracy on validated fixtures. A four-contract architecture separates data custody, model publication, computation, and output release, and supports both a classic chunked path and a streaming path, with the latter reducing mock-measured gas by 37%. An on-chain noisy output oracle emits an encrypted noisy-score handle and a publicly decryptable ternary category, reducing raw score exposure and probing risk. Prototype evaluation on real GWAS fixtures confirms linear gas scaling and suggests that the approach may be cost-competitive in low-gas deployment environments.

Carlos Baquero

Bitcoin price prediction has attracted hundreds of academic papers and continuous social media debate, yet the field lacks consensus on even basic questions: can any model beat a naive "today's price" baseline at horizons of one to six months? We survey the peer-reviewed landscape, categorize papers by evaluation methodology, and contrast academic findings with informal but substantive discourse on X/Twitter. The picture that emerges is sobering. At short-to-medium horizons, no peer-reviewed study has shown robust superiority over the naive baseline across multiple market regimes. Daily predictability is real but does not extend to hourly or monthly horizons, and may not survive transaction costs. The stock-to-flow model has failed formal out-of-sample testing, and Metcalfe's Law valuations have been challenged as spurious. The Bitcoin price power law, while empirically compelling, has not been subjected to formal distributional tests. Meanwhile, social media practitioners raise valid statistical critiques -- ordinary least squares (OLS) violations, backtest overfitting, spurious regressions -- that the academic literature has not formalized. We identify open research directions and propose concrete methodological standards for future work -- walk-forward evaluation, multi-regime holdout windows, naive baseline comparison, inclusion of zero in hyperparameter grids, and Diebold-Mariano significance testing -- arguing that the field's primary need is not more models but better evaluation.

Carlos Baquero, Raquel Menezes

Bitcoin's price has been described as following a power law (PL) in time, $P \sim t^β$ with $\hatβ\approx 5.7$ over 2010-2026. We test this claim using the Clauset-Shalizi-Newman protocol applied to Bitcoin's tail-relevant distributional series, and develop three principled time-domain adaptations of the protocol. We find that (i) the distributional power law is rejected on UTXO balances and daily |returns|, with lognormal preferred decisively; (ii) the fitted time-domain exponent varies by nearly a factor of three across reasonable shifts of the time origin -- it is not specification-robust in the sense required for a shift-invariant structural reading; (iii) standard residual diagnostics and scale-invariance tests proposed in earlier work cannot distinguish a power law from a multi-component sigmoid stack fit to the same data; (iv) Bitcoin price stands apart in a cross-asset comparison spanning Bitcoin on-chain metrics and traditional asset classes: it is the only series in the nine-series in-sample test where no single-component growth curve improves on the power law, and the quarterly $K=3$ wave-stability bootstrap rejects the PL+AR(1) null on Bitcoin at $p = 0.015$ (strict 15% CV threshold) -- a clear cross-asset separation, although not a Bonferroni-robust rejection; and (v) walk-forward Diebold-Mariano evaluation against ten candidates -- including standard time-series baselines (RW with drift, auto-ARIMA, ETS, local-linear-trend) -- shows the in-sample winner (multi-sigmoid) is among the worst long-horizon forecasters, while the simple power law dominates 12-24 month horizons against every standard baseline at $p < 0.05$, precisely because it does not commit to specific wave shapes. The fit-prediction tradeoff is the practical counterpart of the descriptive findings.

Mouhamed Amine Bouchiha, Abdelaziz Amara Korba, Yacine Ghamri-Doudane

Federated learning (FL) has emerged as a promising paradigm for managing electric vehicle (EV) battery data in intelligent transportation systems (ITS), enabling privacy-preserving tasks such as anomaly detection and capacity estimation. However, most existing frameworks rely on centralized aggregation schemes, which pose critical limitations in terms of security and trust. To address these challenges, we propose ABC-DFL, an automated Byzantine-resilient clustered decentralized federated learning (C-DFL) framework for connected EVs. The proposed incentive-driven C-DFL system replaces the central server with an open-permissioned blockchain, featuring a new dynamic Quorum Byzantine Fault Tolerance (QBFT) protocol and an oracle-based aggregation layer, to enhance trust, security, and automation. At the core of ABC-DFL lies FLECA (Filtered Layered Enhanced Clustering Aggregation), a robust hierarchical aggregation protocol that mitigates Byzantine attacks by having each EV filter malicious updates using an adaptive threshold based on deviations from its reference model update. Oracle nodes, responsible for inter-group aggregation, employ robust clustering to isolate and aggregate model updates from trustworthy EV groups. Comprehensive experimental evaluations demonstrate that FLECA matches FedProx convergence under benign conditions and significantly outperforms existing defenses with attack impact scores below 0.10 in adaptive adversarial scenarios. Furthermore, several learning experiments with multitask models confirm the effectiveness and fairness of the incentive mechanism. Finally, on-chain and off-chain benchmarks validate the practicality of ABC-DFL.

Muhammad Farooq Shaikh, Muhammad Saad Iqbal, Davide Piaggio

Introduction Public-sector grant disbursement and fundraising systems are vulnerable to corruption due to limited transparency, weak auditability, unauthorized approvals, and poor traceability of financial transactions. This study proposes a blockchain-based framework to improve accountability and monitoring in public financial management. Methods An Ethereum-compatible ERC-20 test network was used to develop a blockchain-enabled architecture integrating smart contracts, decentralized application (DApp) components, and a Proof-of-Authority (PoA)-based governance mechanism. The framework was evaluated using simulation-based testing, transaction validation scenarios, corruption-detection workflows, and indicative gas-cost analysis under controlled conditions. Results The proposed framework enabled tamper-resistant audit trails, timestamping, and end-to-end traceability of grant transactions. A hierarchical governance model incorporating multi-level institutional approvals reduced unilateral control and improved transaction accountability. Simulation-based evaluation demonstrated improved transparency, auditability, and transaction monitoring efficiency, while maintaining relatively low indicative gas costs. Discussion The findings suggest that blockchain-supported governance mechanisms may strengthen transparency and accountability in public-sector financial systems under controlled conditions. The proposed framework demonstrates the potential of combining institutional governance structures with blockchain-based validation to support secure and traceable grant management and fundraising processes.

Chris Muellerleile, Matt Zook

No abstract is available for this record.

Irada Baghirova

Abstract. Transaction costs are considered one of the key factors determining the efficiency of market mechanisms in modern economic systems. Contract enforcement, data collection and verification, trust assurance between parties, and monitoring mechanisms generate additional costs for economic agents. In particular, in the context of global trade and the digital economy, the increase in these costs can limit the efficiency of market operations. In recent years, the rapid development of blockchain technology has created new institutional and technological opportunities to reduce transaction costs. This distributed ledger technology minimizes the need for intermediaries, ensures data immutability, and enhances transparency in economic relations. This paper analyzes the role of blockchain technology in reducing transaction costs based on economic theory and existing scholarly approaches. Within the research framework, the mechanisms through which blockchain technology addresses information asymmetry, automates contract enforcement, and strengthens trust mechanisms are examined. The main objective of this study is to evaluate the potential efficiency benefits of blockchain technology and scientifically demonstrate its strategic importance in reducing transaction costs.

Yoshimitsu Katayama

This paper presents a personal archive of documented participation in the Bitcoin ecosystem from 2013 to 2026. Scope Declaration: This is an exploratory observational record, not an empirical verification of causal claims. The author registered accounts with MtGox, Bitcoin Foundation, BitcoinStore, Beastoptions, and BuyBitcoin.ph during the earliest phase of Bitcoins civilizational transition. Each registration is timestamped and verifiable through preserved email archives. The author subsequently withdrew from the MtGox ecosystem prior to its 2014 collapse - a decision made on instinct, not analysis. This paper argues that such instinctive withdrawal constitutes a form of cosmic synchronization within the framework of Tendo Economics (V = N/D).

Sachin Bhatt

Transforming legacy SAP systems into smart cloud-based systems is a major shift in today’s digital strategy. This transformation re-engineers old SAP environments, which are often rigid and not easily scalable, by utilizing current technologies, including artificial intelligence and cloud services like AWS. This paper discusses how AI-driven cloud transformation can help organizations transform their SAP ecosystems to be more agile, scalable, and data-driven in their decision-making processes. It addresses enterprise AI, intelligent automation, hybrid cloud environments, and other emerging technologies such as generative AI and distributed ledger systems. The discussion demonstrates how such innovations can be utilized to help create smart enterprises that can make predictions, adapt to changes, and operate more independently. The paper also takes into account the changing role of business analysis and knowledge ecosystems in facilitating this transformation. By integrating these developments and models, this paper provides a comprehensive view of the process of reinventing SAP landscapes to meet the demands of a constantly evolving digital economy.

Juan Minango, A. Paradisi, Bruno Evaristo, Jeffson Sousa

No abstract is available for this record.

GaiXia Wang

This study adopts a conceptual and design-oriented approach to investigate blockchain integration into accounting informatization for improving transparency, reliability, and intelligence in enterprise financial services. A blockchain-based big data model is developed using distributed ledger, consensus, and encryption mechanisms to support secure and consistent financial data sharing. A personalized feedback system empowered by smart contracts and data analytics delivers real-time customized accounting information for service-oriented decision-making. Exploratory independence and reliability assessments examine data consistency and system robustness. Preliminary evidence indicates that the proposed framework tends to reduce data deviations, support decision efficiency, and improve user satisfaction over conventional systems. This study contributes to the service-oriented transformation of accounting informatization and offers a design framework for intelligent data-driven financial management systems enabled by blockchain.

Baptiste Beltzer, Emmanuel Conchon, Sylvain Giroux

In this article, we focus on personal data management in service exchange networks, where members meet each other to share services based on their skills. Through the case study of the Accorderie (a Quebec solidarity cooperative), we propose an innovative protocol designed to reinforce the confidentiality of data relative to members’ addresses and service intervention locations. Using distributed ledger and peer-to-peer interaction, our proposal minimizes the Accorderie’s direct involvement while keeping its position as a trusted authority, allowing members to engage in direct interactions without reliance on a centralized platform. We present three versions of private set membership protocols specially designed to manage locations in the sharing economy. Finally, our findings suggest that decentralized solutions could be a relevant support for solidarity communities, in particular by enhancing member privacy and security, but also by facilitating and reducing maintenance costs.

Gustav Olaf Yunus Laitinen-Fredriksson Lundström-Imanov, N I Abdullayeva

Generative artificial intelligence now synthesizes photorealistic imagery, audio, and video at a cost that defeats traditional forensic intuition. The legal consequences span three regimes studied so far in isolation: international operational law, domestic procedure, and product regulation. This article presents a unified evidentiary framework that maps cryptographic content provenance, robust statistical watermarking, and zero knowledge attestation to the proof requirements of each regime. We define a five tier threat model spanning naive regeneration, adversarial laundering, cross model regeneration, active watermark removal, and insider provenance forgery. We release a public benchmark of 12000 generated items across image, audio, and video modalities under six laundering pipelines for 72000 evaluation samples. We evaluate four representative schemes and report true positive rate at fixed false positive rate, robustness area under the curve, computational overhead, and a regime conditioned legal sufficiency score. We translate empirical detection bounds into legal sufficiency thresholds for command decisions under the law of armed conflict, for criminal and civil admissibility under domestic procedure, and for persistence audits under the European Union Artificial Intelligence Act and analogous regimes. The result is a reproducible reference pipeline, a public benchmark, and model annexes that lawyers, engineers, and operators can deploy together.