This paper addresses the problem of detecting money laundering in the Bitcoin network. Money laundering is the process of handling the proceeds of crime to conceal their illegal source, these illicit transactions have complex features, similar to those of legal transactions. It is well known that transactions can be represented as topological graph structure data, and many GCN-based methods have been developed for Anti-Money Laundering (AML) tasks. However, existing methods have not performed as well in dynamically assigning weights to neighboring nodes and extracting information from global nodes in the Bitcoin network. Therefore, we identify three major challenges: Firstly, GCNs can be misled by concealed illegal transactions due to uniform node representation weights. Secondly, current node-level GCNs cannot handle varied methods of concealing illegal transactions because they fail to extract global information. Thirdly, the costliness of data labelling necessitates the effective use of limited but rich domain-specific labelled data. To address these challenges, we propose the Transformer-enhanced Graph Attention Network (TFGAT) with a Global-Local Attention Mechanism (GLATM) that uses Transformers to extract global information and selectively focus on local information from connected nodes. Due to the limited availability of labelled data from expensive data labelling processes, we introduce a Deep Cyclic Pseudo-Label Updating Mechanism (DCPLU) to enhance data distribution and model robustness, which does not rely on manifold structure or Euclidean distance assumptions. DCPLU can enhance model performance while preserving the model's existing parameters, enabling it to maintain its current faster response time in the application scenario. Experimental results show that our methods outperform existing models across various metrics.
Marielle S. Gross, Ananya Dewan, Mario Macis, Eve Budd · 9 authors
Introduction Organoids are living, patient-derived tumor models that are revolutionizing precision medicine and drug development, however current privacy practices strip identifiers, thereby undermining ethics, efficiency, and effectiveness for patients and research enterprises alike. Decentralized biobanking “de-bi” applies non-fungible tokens (NFTs) to empower privacy-preserving specimen tracking and data sharing for networks of scientists, donors, and physicians. We design, develop, and demonstrate a functional de-bi platform for a real-world organoid biobank. Methods Ethnography of the organoid biobanking ecosystem was performed in 2022–2023, with site visits, interviews, focus groups, and structured observations of stakeholder interactions. An initial ERC-721 prototype was developed and tested, informing the design of a comprehensive NFT model. Web and mobile app prototypes were developed with a suite of ERC-1155 protocols representing ecosystem constituents as NFTs. We demonstrated the platform with publicly available Human Cancer Models Initiatives organoids to establish proof-of-concept for decentralized biobanking as the foundation of a democratized biomedical metaverse, or “biomediverse.” Results Scientists revealed key challenges for organoid research and development under policy, scientific, and economic constraints of the life science landscape. We advanced decentralized biobanking as a blockchain overlay network solution with potential to overcome barriers, enhance utility and unlock value by uniting collaborators in a privacy-preserving biomediverse. Dedicated smart contracts created “soulbound” NFTs as de-identified digital twins of patients, physicians, and scientists in a networked organoid ecosystem. We modeled biospecimen collection, processing, and distribution, including generation and expansion of organoids, via an auditable on-chain mechanism. Key features included the ability to bootstrap the digital twin NFT model onto an established organoid biobank, visibility of patient-linked biospecimens and related research activities for all ecosystem participants, as well as tooling for multisided data exchange. Implementing de-bi with ERC-1155 showed potential to minimize gas costs of on-chain activity vs ERC-721, though complementary layer-2 solutions will be essential for economic viability. Conclusion Decentralized biobanking has the potential to enhance efficiency, increase translational impact and drive research discovery through implementation of NFT digital twins for organoid research networks. Importantly, this approach also bolsters ethical practices by fostering inclusion, ensuring transparency, and enhancing accountability across the research ecosystem. Next steps include live pilot testing, market design research to align stakeholder incentives, and technical solutions to support a sustainable, scalable and mutually rewarding biomediverse.
Evidence plays a crucial role in judicial systems, and managing it securely and efficiently ensures justice. This paper introduces Decentralized Trust, a framework that combines blockchain technology, Non-Fungible Tokens (NFTs), and fog computing to address common issues like tampering, delays, and reliance on centralized systems. Traditional methods that depend on cloud computing often face high latency and slow processing, especially in remote areas. This research also builds upon the challenges identified in previous studies, such as tampering vulnerabilities, inefficiencies in evidence processing, and accessibility issues in underserved regions, providing a novel and comprehensive solution through Decentralized Trust. Fog computing handles tasks closer to where data is created, reducing delays and improving response times. Blockchain ensures that evidence records cannot be altered, while NFTs make each piece of evidence unique and tamper-proof. The framework is organized into layers: edge nodes at police stations capture evidence, fog nodes process the data and create NFTs, and cloud storage, supported by the Interplanetary File System (IPFS), provides secure long-term storage. Results demonstrate that the framework achieves average transaction delays of 24.5 seconds on low-performance devices (Node A) and 168.9 seconds on high-performance devices (Node B), with margins of error showing efficient scalability even under significant processing loads. The observed transaction delays are due to differences in system architecture and processing priorities. High-performance devices (Node B) have more complex validation processes, increased security checks, or resource contention, contributing to longer transaction times. By combining these technologies, Decentralized Trust offers a reliable, fast, and secure way to manage judicial evidence, building trust in the framework while addressing the needs of remote and underserved areas.
State universities and colleges serve as critical pillars of higher education, human capital development, and technological innovation in the Philippines. As publicly funded institutions, they play a vital role in addressing educational disparities, fostering inclusive growth, and advancing research that contributes to national development. This study examines the funding approaches employed by SUCs, assessing their viability amid growing economic pressures and evolving policy landscapes. Traditionally, SUCs rely on direct government appropriations, supplemented by internally generated income from tuition fees, auxiliary services, and entrepreneurial ventures. However, financial constraints, regulatory barriers, and inefficient fund utilization hinder their capacity to achieve long-term sustainability. Drawing insights from international experiences, particularly from OECD and Southeast Asian economies, this study explores higher education financing models and various approaches implemented by public higher education institutions to diversify funding sources. While performance-based funding mechanisms and diversification strategies have gained traction globally, SUCs in the Philippines continue to face challenges in revenue generation due to limited financial autonomy, bureaucratic inefficiencies, and disparities in resource allocation. Government appropriations for SUCs remain unevenly distributed, with significant differences in budget allocation and development funding between institutions. Furthermore, reliance on state subsidies and SUCs’ inability to effectively pursue alternative financing strategies hinder infrastructure investment, research funding, and faculty development. To address these concerns, SUCs have implemented various initiatives, including income-generating projects, commercialization of intellectual property, and university-industry collaborations. However, these efforts have encountered difficulties due to a lack of institutional expertise, regulatory complexities, and insufficient investment from private sector stakeholders. The study highlights how financial autonomy, leadership strategies, and institutional governance affect SUCs' capacity to optimize funding opportunities while pursuing academic excellence. Lessons drawn from Southeast Asian experiences, such as Singapore’s model of funding public universities, Thailand’s policy of higher education decentralization, and Malaysia’s entrepreneurial university approach, offer valuable insights into strengthening SUC financing. Building on international trends and local challenges, this research outlines key policy recommendations for improving financial sustainability among Philippine SUCs. These include rationalizing tuition fees to create more equitable funding structures, expanding university-industry collaboration to boost external revenues, strengthening the commercialization of university-developed technologies, and reforming government budget allocation mechanisms to promote efficiency and innovation. Adopting a more strategic approach to financing can enhance the competitiveness of SUCs, improve higher education quality, and contribute more effectively to national development. A nuanced policy framework is therefore necessary to balance state support with institutional self-sufficiency and foster a robust higher education sector capable of meeting the demands of the global knowledge economy.
Data integrity in Smart Grids (SG) systems can be vulnerable with the implementation of the novel Community Blockchain-Driven Traceability Framework (CBDTF). It enhances Detection Rates (DR), maintains low End-to-End Delay (EED), and uses less energy by using distributed ledger technology and community-based validation. This model deployed a Delegated Proof of Stake (DPoS) consensus mechanism and community-driven testing, resulting in an average Detection Rate (DR) of 98.7% for Data Tampering attacks and a False Positive Rate (FPR) of 1.78%. It outperforms conventional Blockchain (BC) solutions with an EED of 120.8 ms and an average CPU utilization of 1,113 tx/kWh. When compared with conventional Proof-of-Work (PoW), CBDTF requires 60% less energy while proving 96.2% consensus resilience against distinct attacks. Applying real-world SG data collected by a distributed network of 100 nodes, the accuracy of this model was tested. The present study makes a valuable contribution to the field by signifying how BC platforms driven by the public can address SG's data security issues while maintaining the accuracy of real-time operations.
Integrating blockchain into healthcare devices offers potential for improved data control but faces significant usability and acceptance challenges. This study addresses this gap by evaluating CipherPal, an improved blockchain-enabled smart fidget toy prototype, using a multi-framework approach to understand the interplay between technology, design, and user experience. We combined insights from an expert review assessing adherence to Web3 Design Guidelines, a User Acceptance Toolkit assessment with professionals based on UTAUT2, and extended user testing over three days. Findings revealed that users valued CipherPal's satisfying tactile interaction and perceived benefits for well-being, such as stress relief. However, significant usability barriers emerged, primarily related to challenging device-application connectivity, data synchronization, and disruptive physical elements. While conceptually accepted, the blockchain integration mainly added interaction friction and complexity, overshadowing its potential benefits for users during the study. The multi-framework approach proved valuable, providing complementary insights and highlighting tensions between the device's core appeal and usability challenges. This research underscores the critical need for user-centered design in blockchain health applications, emphasizing seamless usability, abstracting technical complexity, and holistically considering physical and social factors.
Patients and healthcare authorities frequently lack confidence in one another when it comes to the security of their medical records in healthcare settings. Particularly when it comes to patient data management, hospitals are infamous for having inadequate security and have long been the target of cyberattacks. Using blockchain technology to store medical records has drawbacks, including an excessive dependence on centralised cloud servers for key storage, privacy concerns and the potential for attackers to deduce personal information about patients based on their blockchain activity. A system where patients have autonomy over their medical records and who can view them is a promising scenario. This article provides a framework for indexing and securing a user’s medical records, with emphasis placed on the healthcare setting using an Ethereum blockchain. The records are secured using biometric authentication and the patient’s Personal Identifiable Information (PII). The patient can grant and revoke access to their records to individual healthcare authorities, and the Interplanetary Name System (IPNS) is used for off-chain record storage. The framework is modular and can be adapted for use in other environments, such as proof of ownership of tickets, and storing travel documents for verification by border control. A smart contract is used to store the hashes of the patient’s iris scans on an Ethereum Virtual Machine (EVM) compatible blockchain. Privacy-preserving identifiers are used to anonymise the patient and where their records are stored on the blockchain. Our approach is to the best of our knowledge the only one that simultaneously offers encryption, anonymity, unlinkability and efficient off-chain storage. Additionally, our approach is the only approach we are aware of that provides record revocability.
The rapid evolution of smart cities has led to transformative advancements through the integration of IoT devices, sensors, and data-driven systems, yet has simultaneously exposed critical vulnerabilities in cybersecurity, data integrity, and trust management. This research proposes a Decentralized Trust Framework that leverages blockchain technology, AI-driven threat detection, and a Lightweight Adaptive Proof-of-Stake (LA-PoS) consensus mechanism to address these challenges. The framework integrates three key layers: a Blockchain Layer for decentralized trust and immutability, a Cybersecurity Layer employing cryptographic standards and AI-based anomaly detection, and a Data Integrity Protocol Layer for real-time synchronization and tamper-proof data validation. Performance evaluations indicate the framework achieves a threefold increase in transaction throughput, a 30% reduction in latency, and enhanced energy efficiency compared to traditional blockchain systems. Security metrics highlight a 98.2% threat detection rate and a substantial reduction in false positives, while resource optimization nearly doubles IoT device battery life. The framework demonstrates applicability in critical smart city use cases, including smart traffic management, energy systems, and public safety, providing secure, scalable, and efficient solutions for urban infrastructures. Despite these advancements, challenges such as interoperability among heterogeneous systems, computational overhead for IoT devices, and policy adoption persist. Future research will focus on optimizing interoperability protocols, incorporating quantum-resistant cryptographic techniques, and extending the framework to emerging domains such as autonomous systems and smart healthcare. The proposed framework provides a robust foundation for building sustainable, resilient, and trustworthy urban ecosystems, bridging gaps in current smart city technologies.
Custom tokens are fundamental in decentralized applications (dApps) operating on Ethereum and other Blockchain platforms. Ethereum, in particular, relies on the ERC-20 standard as a widely accepted token interface, facilitating seamless integration with numerous pre-existing dApps, user interface platforms, and popular web applications like exchange services. A notable security challenge within the ERC-20 framework is the “lost token problem”. This problem arises because users occasionally send tokens to the wrong addresses, and it has caused more than $27 million in damage. In this paper, we evaluate three existing solutions to this issue. Through the utilization of formal modeling, property specification, and the TLC model checker. Most importantly, we propose a novel double-layer solution to remedy the ERC-20 vulnerability. Our formal verification and experimental results indicate our approach encompasses the protection of the already deployed smart contracts, which is a critical aspect that has never been addressed in the existing mitigation techniques.
Open access
Security and Verification in Computing
Radiation Effects in Electronics
Physical Unclonable Functions (PUFs) and Hardware Security
Muhammet Deveci, Dragan Pamučar, Andrei Vasilăţeanu, Gora Datta · 8 authors
The transformative potential of Distributed Ledger Technology (DLT), particularly blockchain, is increasingly recognized in the healthcare sector for its ability to enhance security, transparency, and operational efficiency. This study introduces a novel decision-making framework, the fuzzy Sine Trigonometric COmplex PRo-portional ASsessment (fuzzy ST-COPRAS) model, to prioritize health DLT applications amidst the inherent complexity and uncertainty of healthcare data. The prioritization process is based on a comprehensive evaluation of multi-disciplinary criteria, including economic viability, technological maturity, cybersecurity, and other critical factors. The ranking and prioritization of selected health DLT use cases-such as electronic health records (EHR) management, clinical trial oversight, pharmaceutical supply chain tracking, and telemedicine-are performed using the proposed algorithm, which incorporates tailored survey methods applied to experts in the field. The outcomes of this study are particularly valuable for standardization bodies, companies aiming to invest in DLT for healthcare, and governmental authorities seeking to allocate efficient support mechanisms and develop informed strategies. By identifying high-impact use cases, the findings provide actionable insights that facilitate strategic implementation, resource allocation, and policy development. Furthermore, the study highlights the alignment of blockchain-based solutions with regulatory requirements and techno-economic considerations, fostering trust and compliance. Overall, this research underscores the potential of DLT to revolutionize healthcare through secure, transparent, and efficient systems, driving significant advancements in patient care and system optimization.
We introduce a decentralised, algorithmic framework for permissionless, multi-strategy capital allocation via tokenised, automated vaults. The system is designed to function analogously to a multi-strategy asset management company, but implemented entirely on-chain through a modular architecture comprising four interacting layers. The first, the capitalisation layer, composed of vaults that facilitate multi-asset deposits, tokenises investor participation, and specifies high level risk limits and admissible venues for deployment. The second, the strategy layer, enables the submission of strategies by human developers or autonomous agents, creating a decentralised marketplace governed by a validation mechanism incorporating adversarial and gamified elements. The third, the execution layer, operationalises strategy deployment using the host blockchain network's services. The fourth layer, the validated allocation layer, assesses and allocates capital among validated strategies, dynamically rebalancing toward those exhibiting superior risk-adjusted performance. In the framework, each admitted strategy acts as a manager for the "fund", encapsulated in a smart contract vault that issues transferable V-Tokens, conveying fractional ownership of the real-time portfolio operated by the vault. The system is designed to be open to participation by both human and AI agents, who collectively perform the roles of capital allocators, strategy developers, and validated allocators. The resulting structure is a self-regulating asset management ecosystem capable of decentralised, cooperative optimisation across traditional and digital financial domains. This framework is facilitated by a host chain network, which offers native automation and data oracle services enabling vault entities to autonomously operate on-chain, paving the way for being self sufficient in dynamic allocation of capital.
This paper presents a complete formal specification, protocol description, and mathematical proof structure for Simplified Payment Verification (SPV) as originally defined in the Bitcoin whitepaper \cite{nakamoto2008}. In stark contrast to the misrepresentations proliferated by popular implementations, we show that SPV is not only secure under bounded adversarial assumptions but strictly optimal for digital cash systems requiring scalable and verifiable transaction inclusion. We reconstruct the SPV protocol from first principles, grounding its verification model in symbolic automata, Merkle membership relations, and chain-of-proof dominance predicates. Through rigorous probabilistic and game-theoretic analysis, we derive the economic bounds within which the protocol operates securely and verify its liveness and safety properties under partial connectivity, hostile relay networks, and adversarial propagation delay. Our specification further introduces low-bandwidth optimisations such as adaptive polling and compressed header synchronisation while preserving correctness. This document serves both as a blueprint for secure SPV implementation and a rebuttal of common misconceptions surrounding non-validating clients.
This paper examines whether Bitcoin realized volatility admits a measurable pathwise roughness index and how stable that estimate is across time and measurement designs. Using one-minute BTC/USD close prices from Bitstamp between 2017 and 2024, realized-volatility paths are constructed at 1-, 5-, 10-, and 15-minute frequencies and evaluated with the model-free normalized p-variation estimator of Cont and Das (2024). A unique root is obtained in 341 of 380 rolling 90-day window-frequency configurations, or 89.7 percent, and in 113 of 128 non-overlapping configurations, or 88.3 percent. Conditional rolling medians of the roughness estimate are 0.054, 0.065, 0.086, and 0.080 at the four respective frequencies, and all finite estimates from the temporal, window-length, and jump-robust analyses are below 1/2. Root availability and estimate magnitude nevertheless vary across periods and measurement procedures. Truncation affects root availability primarily at one minute, while bipower variation yields a unique root in 20 of 24 eligible fixed-window configurations. In the eight five-minute fixed-window samples, comparisons with iterative amplitude-adjusted Fourier transform surrogates identify excess MF-DFA width in three samples and excess log-moment curvature in one. Bitcoin realized volatility therefore generally admits a low pathwise roughness estimate, but that estimate is not invariant to time or measurement design. The results concern observed realized volatility and do not directly identify the roughness of latent spot volatility.
Shahida Hafeezan Qureshi, Saif Ur Rehman Malik, Junaid Haseeb, Syed Atif Moqurrab · 6 authors
ABSTRACT Federated Learning (FL) is emerging as a premier paradigm for privacy‐preserved Machine Learning (ML), enabling devices to train models without central data pooling collaboratively. In the contemporary Internet of Things (IoT) landscape, characterized by escalating energy consumption and associated carbon footprint, FL is recognized not merely for its privacy features. Intrinsic to decentralized architectures such as FL, secure communication is based on digital signatures to guarantee integrity. This is particularly evident in sensitive sectors such as the Internet of Vehicles (IoV), banking, and healthcare. Integrating FL becomes imperative and intricate as these sectors are intertwined with the IoT fabric. Our study unveils “Secure Federated Learning Framework (SecFL),” a pioneering decentralized framework combining FL and sustainable computing. SecFL offers defences against adversarial attacks such as data poisoning and label flipping. Utilizing the Rivest‐Shamir‐Adleman (RSA) asymmetric encryption algorithm for securing digital communications and transactions, combined with ElGamal encryption and a private Ethereum blockchain, ensures enhanced client‐specific security. Our research emphasizes the formal modeling of adversarial dynamics using High‐Level Petri nets (HLPN) within the FL‐IoT ecosystem, balancing system dynamics and energy conservation. Our model consistently outperforms contemporary solutions in accuracy and time efficiency after validation. As IoT burgeons into domains like environmental monitoring, smart cities, and energy grids, the SecFL framework, fostering FL, optimizes energy utilization and bolsters resource efficiency. In our comparative analysis, the Elliptic Curve Digital Signature Algorithm (ECDSA) algorithm demonstrates superior transaction latency and verification time compared to RSA and Elliptic Curve Cryptography (ECC).
Dragos Gabriel Miloșvici, Claudia Lazur, Lacramioara Mansour
Digital currencies provide multiple opportunities for attractive businesses, offering investments and secure transactions through blockchain. Additionally, they can be owned and traded without restrictions on quantity or amount, both by individuals and legal entities, thus simplifying and diversifying payment processing. At the same time, digital payments are vulnerable to cyberattacks, which can affect the confidentiality of personal data. The most well-known virtual currency is Bitcoin, invented in 2008 by Satoshi Nakamoto and published in 2009. It opens a new path toward the digital payment system, where peer-to-peer payments can be made almost instantly. Bitcoin has seen a sharp increase, and by eliminating transaction costs and intermediary control, it has encouraged more individuals and businesses to use this cryptocurrency. The purpose of this paper is to present the emergence, nature, regulations, advantages, and disadvantages of cryptocurrencies, as well as their taxation
The swift advancement of blockchain technology has introduced a transformative innovation known as smart contracts, which are self-enforcing, unchangeable computer programs for agreements. While these contracts offer benefits like efficiency and openness, their inherent qualities present major hurdles for protecting consumers, especially from the risk of inequitable terms being included. This study aims to deeply investigate the strengths and weaknesses of current Indonesian law in offering legal safeguards to consumers who use smart contracts for their transactions. Utilizing a normative juridical methodology with a statutory and conceptual framework, the research reveals several key findings. First, the essential features of smart contracts, most notably their unchangeable and self-enforcing nature, are in direct opposition to the adaptable and justice-focused principles of Indonesian contract law, like the doctrine of good faith. Second, although a foundational level of protection is offered by the Indonesian Civil Code (KUHPerdata), the Consumer Protection Law (UUPK), and the Law on Information and Electronic Transactions (UU ITE), substantial legal vacuums and difficult enforcement problems persist. Third, the research pinpoints specific ways unfair clauses appear as functions within the code and confirms that applying a purposeful interpretation of current legislation can help lessen their negative effects. In conclusion, this paper asserts the pressing requirement for creating specific legal rules and bolstering institutional supervision, especially by the Financial Services Authority (OJK), to ensure that consumer rights remain protected amidst the evolution of contractual technology.
The Internet of Things (IoT) has become an integral part of daily life, making the protection of user privacy increasingly important. In gateway-based IoT systems, user data is transmitted through gateways to platforms, pushing the data to various applications, widely used in smart cities, industrial IoT, smart farms, healthcare IoT, and other fields. Threshold Public Key Encryption (TPKE) provides a method to distribute private keys for decryption, enabling joint decryption by multiple parties, thus ensuring data security during gateway transmission, platform storage, and application access. However, existing TPKE schemes face several limitations, including vulnerability to quantum attacks, failure to meet Simulation-Security (SS) requirements, lack of verifiability, and inefficiency, which results in gateway-based IoT systems still being not secure and efficient enough. To address these challenges, we propose a Verifiable Simulation-Secure Threshold PKE scheme based on standard Module-LWE (VSSTPM). Our scheme resists quantum attacks, achieves SS, and incorporates Non-Interactive Zero-Knowledge (NIZK) proofs. Implementation and performance evaluations demonstrate that VSSTPM offers 112-bit quantum security and outperforms existing TPKE schemes in terms of efficiency. Compared to the ECC-based TPKE scheme, our scheme reduces the time cost for decryption participants by 72.66%, and the decryption verification of their scheme is 11 times slower than ours. Compared with the latest lattice-based TPKE scheme, our scheme reduces the time overhead by 90% and 48.9% in system user encryption and decryption verification, respectively, and their scheme is 13 times slower than ours in terms of decryption participants.
Yu Zhang, Yafei Li, Jufang Zhang, Claudio J. Tessone
When analyzing the balance distribution of Bitcoin users, we found that it follows a log-normal pattern based on a rigorous Uniformly-Most-Powerful-Unbiased test. Drawing parallels from the successful application of Gibrat’s law in explaining city size and word frequency distributions, we tested whether a similar principle could account for the log-normal distribution in Bitcoin balances. However, our calculations revealed that the exponent parameters in both the drift and variance terms deviate slightly from 1 when applying Geometric-Brownian-Motion on the Bitcoin balance, which means that Bitcoin users’ balance distribution cannot be explained only by the proportional growth rule alone. During this exploration, Bitcoin users’ behaviors are also investigated. We discovered an intriguing phenomenon: Bitcoin users tend to fall into two distinct categories based on their transaction behavior, which we refer to as “poor” and “wealthy” users. Poor users who initially purchase only a small amount of Bitcoin tend to buy more Bitcoins first and then sell out all their holdings over time. The certainty of selling all their coins is higher and higher with time. In contrast, wealthy users who acquire a large amount of Bitcoin from the start tend to sell off their holdings over time. The speed at which they sell their Bitcoins is lower and lower over time. The wealthier the user, the larger the proportion of their balance and the higher the certainty they tend to sell their holdings. This research provided a new perspective to explore Bitcoin users’ behaviors which may apply to other finance markets.
This paper presents the design and implementation of a decentralized electronic voting system based on a hybrid architecture that integrates the TRON blockchain with off-chain authentication mechanisms.The proposed solution employs smart contracts written in Solidity to record votes in an immutable and publicly auditable manner, while a backend service implemented in Node.js and a MySQL database handles voter authentication and enforces voter uniqueness.To prevent duplicate voting and ensure auditability, cryptographic hash functions are used to bind voter credentials and election parameters to each vote without exposing sensitive data on-chain.Experimental results demonstrate that the system effectively mitigates common security threats, such as duplicate voting and unauthorized data manipulation, while maintaining low transaction costs and practical usability.The findings indicate that the proposed hybrid approach provides a secure, transparent, and cost-effective alternative for electronic voting systems in real-world scenarios.
Cryptocurrency-related crimes are on the rise and have a wide-ranging impact across various areas. To effectively combat and prevent such crimes, cryptocurrency forensics, which relies on blockchain analysis, is essential. Despite advancements in Bitcoin de-anonymization techniques, several challenges persist. The absence of authentic data labels introduces uncertainty in de-anonymization results, especially in the context of address clustering. This issue is further compounded by the development of privacy-enhancing technologies that obscure address linkages, thus undermining the reliability of outcomes as forensic evidence. To address these limitations, this study focuses on Bitcoin blockchain analysis and the improvement of address clustering. Specifically, the work presents an enhanced simulation model designed to accurately simulate real Bitcoin transactions, offering a stable platform for evaluating address clustering algorithms that utilize transaction details, thereby facilitating the assessment of the admissibility of clustering results. Meanwhile, we introduce a new heuristic algorithm aimed at identifying one-time change addresses, with experimental results demonstrating that it achieves more precise clustering outcomes than existing heuristic methods. Furthermore, our blockchain analysis reveals overarching patterns and recent changes in the Bitcoin blockchain, particularly following the introduction of the BRC-20 token.
With the rapid growth of healthcare data and the need for secure, interpretable, and decentralized machine learning systems, Federated Learning (FL) has emerged as a promising solution. However, FL models often face challenges regarding privacy preservation, transparency, and resistance to adversarial attacks. To address these limitations, this paper proposes the Privacy Preserving Federated Blockchain Explainable Artificial Intelligence Optimization (PPFBXAIO) framework, which integrates blockchain technology, Explainable AI (XAI), and optimization techniques to ensure privacy, traceability, and robustness in FL-based systems. PPFBXAIO employs Secure Hash Algorithm 256 (SHA-256) for blockchain-backed secure model updates, Min-Max normalization for feature scaling, and the Levy Grasshopper Optimization Algorithm (LGOA) for optimal feature selection and federated model tuning. The Entropy Deep Belief Network (EDBN) is used as the classifier to enhance classification accuracy and detect attacks. XAI tools like SHAP are utilized to improve model interpretability. Experimental validation was conducted using the Heart Disease dataset from Kaggle and the Wisconsin Breast Cancer dataset. Results showed that PPFBXAIO achieved 95.07% accuracy, 95.44% precision, 96.54% recall, 95.98% F1 score, and reduced training loss by 4.93% for Breast Cancer Wisconsin and achieved 93.07% accuracy, 91.19% precision, 95.39% recall, 93.24% F1 score for Heart Disease dataset. Proposed system has reduced latency by 81 ms, and improved throughput by 109 transactions per second for 100 rounds as compared to traditional models like FedAvg, FL-MPC, FL-RAEC, and PEFL. These results highlight the framework's superior performance, privacy preservation, and practical applicability in decentralized healthcare AI systems.
Open access
Privacy-Preserving Technologies in Data
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare and Education
The integration of smart contracts and artificial intelligence (AI) into family law represents a major advancement in the digital transformation of legal procedures for marriage contracts. The blockchain technology enables smart contracts to function autonomously as self-executing agreements that deliver benefits through automated processes and transparent systems, and secure transactions. AI integration with these agreements enables real-time adjustments through adaptability because it allows automatic changes based on financial, legal, or personal circumstances. The implementation of family law through these agreements creates essential legal problems regarding their enforceability and jurisdictional differences, and their ability to handle marital relationship dynamics. The paper studies the basis of smart contracts alongside their potential AI-enhanced adaptability and automation capabilities. It also studies the Serbian marriage contract legal regulation. The research investigates the legal obstacles and jurisdictional problems that emerge when these technologies are used in family law by making comparisons with other civil law jurisdictions. The article also evaluates important ethical issues related to algorithmic bias and privacy concerns before it concludes by analysing the advantages and disadvantages of AI-enhanced smart contracts for marriage contracts in Serbia.
Internet of Things (IoT) has also brought about rapid adoption of technologies in agriculture that provides real-time monitoring and management of crop production, water usage, and soil health. Nevertheless, IoT devices combined with blockchain-based smart contracts create severe energy consumption issues, particularly when the resource is limited. This paper will present a framework that is energy-efficient to implement smart contracts in the network of IoT devices in the agriculture industry. “The proposed solution allows minimizing the computational overhead, preserving data integrity, transparency, and security by improving consensus mechanisms and scheduling transactions. According to the experimental simulations, the framework reduces the energy use by up to 35 percent of that of the existing blockchain execution models, without affecting the operational reliability. The results offer practical lessons towards sustainable precision farming whereby technological innovation is balanced with the environment.
Michael Smith, Valerie Kilders, Todd Kuethe, Nicole Olynk Widmar
We examine the relationship between market performance of leading cryptocurrencies (Bitcoin and Ethereum), meme-stocks (AMC, GameStop), and subjects of corporate boycotts (Bud Light) using weekly market price and volume data along with social media data of weekly mentions (which total 337 million in this dataset) and net sentiment. Using vector autoregression (VAR) time series analysis along with Granger causality testing and structural breaks, we successfully predict trade volume of these various assets using social media data and price data. We also find that closing price data and trade volume are reliable predictors of net sentiment about crypto in online and social media. However, we struggle to predict the closing price for the group of assets studied. We also employ impulse response functions, finding evidence of a dynamic relationship occurring between online and social media net sentiment and online media volume with closing price and trade volume. These functions show that investor sentiment operates with a short memory lasting around 3 weeks, additionally these functions show that price generates a shock on trade volume but that crypto and meme-stock markets experience this differently. Our findings reinforce the notion that meme-stock traders and herd investors do not trade on market fundamentals but are instead sensitive to herding (or sentiment) movements. Our findings also suggest that compared to these meme-stock investors, crypto markets have more traditional motivations of loss aversion.