The evolution of technology has sparked significant interest in transforming traditional voting into efficient, secure online systems.This study introduces a novel approach that enhances voter privacy and data security by utilizing a UniqueBlend ID algorithm to generate unique identifiers for voters, obscuring Aadhar numbers and preventing identity disclosure.Blockchain technology is integrated to enhance transparency, eliminate fraud, and create an immutable voting record.However, integrating decentralized applications (dApps) with legacy web2 systems presents challenges in data storage and retrieval.To address these issues, this research presents Optima, an interface that simplifies data segregation between web2 and web3 storage systems using a JSON-based structure.Optima optimizes storage efficiency, minimizes gas fees, and reduces development overhead, allowing developers to focus on application logic.This streamlined data segregation approach significantly improves the efficiency and security of online voting, ensuring voter anonymity and maintaining the integrity of the voting process.
У статті проаналізовано ключові недоліки централізованих афілійованих платформ, зокрема брак прозорості, складність виплат і надмірні витрати на інтеграцію. Запропоновано інтеграцію Web3-технологій (блокчейну, смарт- контрактів) як ефективну альтернативу для підвищення довіри та оптимізації процесів, що підтверджується попередніми дослідженнями. Робота наголошує на відсутності детальних методів та моделей інтеграції Web3-технологій в системи афілійованого маркетингу і формулює низку дослідницьких питань, які охоплюють криптографію, розробку смарт-контрактів, графовий аналіз взаємодій та OO-моделювання децентра- лізованих застосунків. Представлено методологічний підхід, що складається з аналізу існуючих моделей, огляду літератури, розробки Web3-базованої системи та формаль- ного тестування прототипів. Бібл. 8, іл. 2, табл. 1
Rowdy Chotkan, Bulat Nasrulin, Jérémie Decouchant, Johan Pouwelse
Spam poses a growing threat to blockchain networks. Adversaries can easily create multiple accounts to flood transaction pools, inflating fees and degrading service quality. Existing defenses against spam, such as fee markets and staking requirements, primarily rely on economic deterrence, which fails to distinguish between malicious and legitimate users and often exclude low-value but honest activity. To address these shortcomings, we present StarveSpam, a decentralized reputation-based protocol that mitigates spam by operating at the transaction relay layer. StarveSpam combines local behavior tracking, peer scoring, and adaptive rate-limiting to suppress abusive actors, without requiring global consensus, protocol changes, or trusted infrastructure. We evaluate StarveSpam using real Ethereum data from a major NFT spam event and show that it outperforms existing fee-based and rule-based defenses, allowing each node to block over 95% of spam while dropping just 3% of honest traffic, and reducing the fraction of the network exposed to spam by 85% compared to existing rule-based methods. StarveSpam offers a scalable and deployable alternative to traditional spam defenses, paving the way toward more resilient and equitable blockchain infrastructure.
An automated market maker (AMM) provides a method for creating a decentralized exchange on the blockchain. For this purpose, individual investors lend liquidity to the AMM pool in exchange for a stream of fees earned from its operations as a market maker. Within this work, we reinterpret the loss-versus-rebalancing as the implied fee stream generated by an AMM so that a risk-neutral investor is indifferent in the decision of providing liquidity. With this implied fee structure, we propose a novel fixed-for-floating swap on the fees generated by an AMM in order to quote the implied volatilities and implied correlations of digital assets. We apply this theory to realized fees in different markets to empirically validate the relevance of the deduced fee-based volatility.
Blockchain Business applications and cryptocurrencies such as enable secure, decentralized value transfer, yet their pseudonymous nature creates opportunities for illicit activity, challenging regulators and exchanges in anti money laundering (AML) enforcement. Detecting fraudulent transactions in blockchain networks requires models that can capture both structural and temporal dependencies while remaining resilient to noise, imbalance, and adversarial behavior. In this work, we propose an ensemble framework that integrates Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), and Graph Isomorphism Networks (GIN) to enhance blockchain fraud detection. Using the real-world Elliptic dataset, our tuned soft voting ensemble achieves high recall of illicit transactions while maintaining a false positive rate below 1%, beating individual GNN models and baseline methods. The modular architecture incorporates quantum-ready design hooks, allowing seamless future integration of quantum feature mappings and hybrid quantum classical graph neural networks. This ensures scalability, robustness, and long-term adaptability as quantum computing technologies mature. Our findings highlight ensemble GNNs as a practical and forward-looking solution for real-time cryptocurrency monitoring, providing both immediate AML utility and a pathway toward quantum-enhanced financial security analytics.
The digital forensic investigation process overwhelmingly depends on the unbroken, tamper-proof, and audit able Chain of Custody for evidence data. However, most traditional Chain of Custody systems suffer limitations being either static permission control, weak traceability, or even worse lack implementation of cryptographically enforced privacy and integrity guarantees in evidence lifecycle management. These failures can disable real-time, transparent, and secure evidence life cycle management, especially in costly, heterogeneous, and multi-party environments. The work introduces FAIR-CoC, a Forensic Adaptive Integrity and Reputation Chain-of-Custody system conceptually based on the Hybrid Blockchain-IPFS CoC Ledger (HBI-CoC) architecture to resolve these problems. This system uses IPFS as a decentralized repository for forensic artifacts while using private Ethereum blockchain for immutable record keeping of cryptographic hashes, access metadata, and smart contract logic. Furthermore, the framework consists of five key parts, ZK-TIV (Zero-Knowledge Temporal Integrity Verifier) enables evidence access within permissible timestamp windows using zk-SNARK proofs without revealing accessor identities to enhance privacy and temporal accountability in the process; the MV-PGChain (MultiVector Provenance Graph Chain) builds high fidelity provenance graph capturing handler, location, tool, and timestamp changes, with Merkle root snap-shots anchored on-chains; AWReS (Access Weighted Reputation Scorer) dynamically assesses trustworthiness for custodians using on-chain behavioral analytics; HAT-FSS (Homomorphic Audit Tags for Forensic Shard Storage) allows encrypted auditability for IPFS-stored shards using homomorphic verification tags; PA-ESC (Predictive Access Escalation Smart Contracts) embeds AI-based access behavior modeling to automate privilege revocation or escalations. Collectively, these functionalities present a novel adaptive and privacy-preserving CoC framework with solid integrity, traceability, and trust guarantees. Experimental evaluations contend with low latency and accuracy whether across all modules, establishing FAIR-CoC as a leap forward toward secure, scalable, and intelligent forensic chain-ofcustody systems.
With the acceleration of urbanization and the promotion of the “dual carbon” goal, the road transport system is facing the triple challenges of efficiency bottlenecks, excessive carbon emissions, and data security risks. In view of the shortcomings of the existing research in dynamic response, multi-objective collaboration and privacy protection, this paper proposes a three-in-one intelligent management framework: (1) construct a real-time dynamic path optimization model based on Deep Reinforcement Learning (DRL), and realize the precise regulation of traffic flow through multi-source data fusion and adaptive reward mechanism; (2) Design a multi-objective optimization model integrating carbon trading mechanism to quantify the synergistic relationship between transportation efficiency, carbon emissions and economic costs; (3) Develop a distributed data management framework based on blockchain, and use zero-knowledge proof and smart contract technology to protect user privacy. The peak simulation experiment based on the fifth ring road section of Beijing shows that the proposed method reduces the average traffic time by 18.7%, the carbon emission by 23.5%, and the risk of data leakage by 76% compared with the traditional algorithm. This study provides theoretical and technical support for the construction of a safe, efficient and low-carbon intelligent transportation system.
, a novel, multi-layered framework designed to overcome these critical limitations in the Medical IoT domain. Med-Q Ledger integrates a permissioned Hyperledger Fabric for transactional integrity with a scalable Holochain Distributed Hash Table for high-volume telemetry, achieving horizontal scalability and sub-second commit times. To fortify long-term data security, the framework incorporates post-quantum cryptography (PQC), specifically CRYSTALS-Di lithium signatures and Kyber Key Encapsulation Mechanisms. Real-time, privacy-preserving intelligence is delivered through an edge-based federated learning (FL) model, utilizing lightweight autoencoders for anomaly detection on encrypted gradients. We validate Med-Q Ledger's efficacy through a critical application: the prediction of intestinal complications like necrotizing enterocolitis (NEC) in preterm infants, a condition frequently necessitating emergency colostomy. By processing physiological data from maternal wearable sensors and infant intestinal images, our integrated Random Forest model demonstrates superior performance in predicting colostomy necessity. Experimental evaluations reveal a throughput of approximately 3400 transactions per second (TPS) with ~180 ms end-to-end latency, a >95% anomaly detection rate with <2% false positives, and an 11% computational overhead for PQC on resource-constrained devices. Furthermore, our results show a 0.90 F1-score for colostomy prediction, a 25% reduction in emergency surgeries, and 31% lower energy consumption compared to MQTT baselines. Med-Q Ledger sets a new benchmark for secure, high-performance, and privacy-preserving IoMT analytics, offering a robust blueprint for next-generation healthcare deployments.
In the Internet of Vehicles (IoV) network, numerous vehicle terminals are required to continuously upload local data to maintain the latest service models, which supports intelligent transportation and personalized services. However, the privacy risks posed by this continuous data uploading cannot be ignored. Federated learning, as a distributed ma-chine learning paradigm, enables global model training without sharing original data. The introduction of blockchain further supports decentralization and immutability. However, federated learning also faces the risk of poisoning attacks, where malicious clients may upload abnormal or tampered model updates, severely impacting global model performance. To address this, this paper proposes a security framework that combines zero-knowledge proofs, federated learning, and blockchain. Clients use zero-knowledge proofs to ensure the legitimacy of uploaded updates, while the blockchain is responsible for verification and storage. Ultimately, a robust global model is obtained through federated aggregation. Experimental results demonstrate that this scheme effectively resists poisoning attacks, significantly improving system security and reliability while protecting user privacy.
India’s democracy allows citizens to make representatives accountable during elections, but no immediate mechanism enables them to remove representatives prior to a particular term ending if representatives are seriously underperforming or categorized as nonrepresentative. The Right to Recall (RTR) E-Voting System utilizing Blockchain Technology addresses this lack of an immediate mechanism, providing a safe, transparent, and performance-based recall mechanism that decades of political science literature have deemed necessary. Recalling representatives for statutory reasons is often a slow bureaucratic process that is influenced by political bias when it is allowed at all. Blockchain’s decentralized, immutable, and secure nature helps guarantee that votes are tamper-proof, verifiable, and transparent. The system employs smart contracts to automate much of the recall processes while reducing human intervention and our potential for manipulation. Existing cryptographic techniques such as Elliptic Curve Cryptography (ECC) and Zero-Knowledge Proofs (ZKP) guarantee a high level of security and voter anonymity in our proposed system. This paper describes the design, architecture, implementation challenges, and the impact of a blockchain technology-enabled RTR voting platform in the context of India.
Blockchain technology presents a transformative solution to the vulnerabilities inherent in traditional electoral processes by ensuring security, transparency, and scalability. By leveraging a decentralized and immutable ledger, blockchain provides vote integrity, voter anonymity, and end-to-end verifiability. This paper details the design, implementation, and evaluation of a blockchain-based online voting system using Ethereum smart contracts. The system integrates sophisticated cryptographic techniques, including zero-knowledge proofs and homomorphic encryption, alongside a distributed architecture to enhance election integrity. Our implementation demonstrates several key advantages: tamper-proof voting records, real-time verifiability of election progress, and scalability for large electorates. Performance analysis reveals an 85% reduction in vote counting time compared to traditional methods, with 99.99% accuracy and zero security breaches during testing. The system achieves cost efficiency through automated vote tallying while maintaining voter privacy. While challenges such as regulatory compliance and user adoption persist, this work demonstrates that blockchain technology holds the potential to revolutionize democratic processes by offering a secure, transparent, and accessible voting solution for the digital age.
This paper introduces Lyquor, a decentralized platform that reimagines blockchain infrastructure through a service-centric model where nodes selectively host smart contracts (called Lyquids) while preserving global composability. We present three key innovations: (1) Fate-Constrained Ordering (FCO), which decouples consensus from execution to enable selective hosting without sacrificing Layer-1 grade composability; (2) Direct Memory Architecture (DMA), which eliminates state access bottlenecks by providing each contract with persistent, byte-addressable virtual memory; and (3) Universal Procedure Call (UPC), which enables fault-tolerant, programmable coordination across distributed off-chain computation. Together, these components are powered by a Rust-macroed unified programming model where on-chain and off-chain logic coexist seamlessly, supporting both traditional smart contract patterns and novel distributed applications. Lyquor addresses critical limitations in existing systems while maintaining compatibility with Ethereum APIs, offering a path toward truly scalable decentralized computation.
With the development of science and technology, the Internet of Things (IoT) had been integrated into the daily life of people. That makes the security of IoT a necessity and gains more attention. The Device authentication is an important issue in the security of IoT. In this paper, we propose a device authentication scheme based on both Physically Unclonable Function (PUF) and zero-knowledge proof. The proposed mutual authentication scheme reduces the memory load on the server and provides both data integrity and confidentiality during the authenticating process. We verify the proposed device authentication algorithm on the IoT platform Raspberry Pi using SRAM-PUF. The experimental results reveal that the proposed device authentication scheme is novel for IoT. It can resist brute force attack, replay attack, man-in-the-middle attack, machine learning attacks, and etc.
Physical Unclonable Functions (PUFs) and Hardware Security
Access to safe and sustainable drinking water services remains a significant challenge, particularly in rapidly urbanizing and climate-impacted urban and peri-urban areas. Traditional utilities, often operating under public-private partnerships, are frequently constrained by underinvestment, fragmented governance, and weak accountability, resulting in service gaps. Decentralized Drinking Water Services (DWS) offer adaptable, user-centered alternatives through locally tailored technologies and alternative water sources. However, their adoption is constrained by financial barriers, regulatory uncertainty, and limited community engagement. While previous studies have emphasized the technical and operational aspects of DWS, this study addresses a gap by examining how business model innovation can enable long-term adoption and scalability. Using a mixed-methods approach, combining a systematic literature review with interviews of private-sector providers, this research identifies Product-Service Systems, Social Enterprises, and Community-Based Management as key business models. Findings reveal that hybrid approaches, which bridge formal/informal, centralized/decentralized, and public/private boundaries, are central to navigating heterogeneous infrastructure configurations in diverse contexts. Providers must balance financial viability with equitable access, adapt to varying institutional capacities, and respond to evolving policy landscapes. The analysis shows that regions with advanced water infrastructure industries often shape service models in markets with more constrained resources, underscoring the need to adapt these transfers to local socio-political and cultural contexts. Scalable DWS delivery depends not only on technological innovation but also on coherent regulatory frameworks, inclusive governance, and financing strategies aligned with community affordability. Finally, the study calls for future policy efforts that prioritize coherent regulatory frameworks, including clear quality standards, permitting procedures for alternative water sources, mechanisms for public oversight, and community engagement to ensure long-term functionality and local ownership. • Identifies business models enabling Decentralized Drinking Water Services in urban and peri-urban areas. • Combines a systematic literature review with private-sector interviews. • Shows hybrid models bridging formal/informal and centralized/decentralized systems. • Highlights policy and regulations as key to scaling decentralized solutions. • Finds financial viability and equitable access as central trade-offs for providers.
Ethereum’s introduction of smart contracts has significantly expanded blockchain use cases, enabling decentralized applications. Since all transactions are publicly available, the system can be modeled as a complex network, allowing us to uncover emergent user behavior and explore the underlying dynamics of the ecosystem. In this study, we focus on analyzing the structural differences within the Ethereum system across three distinct market regimes: bull, bear, and sideways. To achieve this, we apply a Hidden Markov Model to the log-return time series to uncover the underlying states, revealing three differentiated states, each corresponding to a specific market regime. Next, we investigate the network structural differences across these regimes, finding meaningful variations. During the bear regime, the out-degree distribution is more heterogeneous, with the largest hub exhibiting more extreme out-degree values. Additionally, during the bull and sideways regimes, we observe higher levels of reciprocity, clustering, and modularity compared to the bear regime. These findings suggest that during bull and sideways markets, the interaction patterns are more complex, and the community structure is more cohesive. Overall, our work underscores how market conditions shape trading patterns and the structural properties of the Ethereum transaction network, providing new insights into the interplay between market regimes, network topology, and user behavior in decentralized ecosystems.
В статье рассматриваются теоретические и прикладные аспекты стратегической цифровой трансформации бизнеса с акцентом на потенциал технологий Web3 в корпоративной и региональной экономике. Проведен сравнительный анализ эволюции веб-технологий от Web 1.0 до Web 3.0, обоснована актуальность интеграции Web3-инструментов (токенизация, DAO, NFT, децентрализованные платформы) в программы цифровизации холдинговых структур и региональных цифровых кластеров. На основе анализа статистических данных и международных кейсов выявлены ключевые барьеры и точки роста для внедрения Web3 в управленческие и экономические процессы. Сформулированы рекомендации по нормативному сопровождению, внедрению пилотных проектов и развитию компетенций в области децентрализованных архитектур. Статья ориентирована на исследователей, управленцев и стратегов, заинтересованных в практическом применении Web3 в целях устойчивого развития цифровой экономики. This article explores the theoretical and practical aspects of strategic digital transformation in business, focusing on the potential of Web3 technologies within corporate and regional economies. A comparative analysis of the evolution of web technologies from Web 1.0 to Web 3.0 is provided, emphasizing the relevance of integrating Web3 tools – such as tokenization, DAOs, NFTs, and decentralized platforms – into the digitalization programs of holding structures and regional clusters. Based on statistical data and international case studies, the article identifies key challenges and growth opportunities for embedding Web3 into management and economic processes. Recommendations are formulated for regulatory support, pilot project implementation, and competence development in decentralized architectures. The study is intended for researchers, managers, and strategists interested in the practical application of Web3 to foster a sustainable digital economy.
Yousef Tahboub, Anthony Revilla, Jaydon Lynch, Greg Floyd
Casting a ballot from a phone or laptop sounds appealing, but only if voters can be confident their choice remains secret and results cannot be altered in the dark. This paper proposes a hybrid blockchain-based voting model that stores encrypted votes on a private blockchain maintained by election organizers and neutral observers, while periodically anchoring hashes of these votes onto a public blockchain as a tamper-evident seal. The system issues voters one-time blind-signed tokens to protect anonymity, and provides receipts so they can confirm their vote was counted. We implemented a live prototype using common web technologies (Next.js, React, Firebase) to demonstrate end-to-end functionality, accessibility, and cost efficiency. Our contributions include developing a working demo, a complete election workflow, a hybrid blockchain design, and a user-friendly interface that balances privacy, security, transparency, and practicality. This research highlights the feasibility of secure, verifiable, and scalable online voting for organizations ranging from small groups to larger institutions.
Andrés Fábrega, Amy Zhao, Jay Yu, James Austgen · 8 authors
Decentralized Autonomous Organizations (DAOs) use smart contracts to foster communities working toward common goals. Existing definitions of decentralization, however -- the 'D' in DAO -- fall short of capturing the key properties characteristic of diverse and equitable participation. This work proposes a new framework for measuring DAO decentralization called Voting-Bloc Entropy (VBE, pronounced ''vibe''). VBE is based on the idea that voters with closely aligned interests act as a centralizing force and should be modeled as such. VBE formalizes this notion by measuring the similarity of participants' utility functions across a set of voting rounds. Unlike prior, ad hoc definitions of decentralization, VBE derives from first principles: We introduce a simple (yet powerful) reinforcement learning-based conceptual model for voting, that in turn implies VBE. We first show VBE's utility as a theoretical tool. We prove a number of results about the (de)centralizing effects of vote delegation, proposal bundling, bribery, etc. that are overlooked in previous notions of DAO decentralization. Our results lead to practical suggestions for enhancing DAO decentralization. We also show how VBE can be used empirically by presenting measurement studies and VBE-based governance experiments. We make the tools we developed for these results available to the community in the form of open-source artifacts in order to facilitate future study of DAO decentralization.
Given a universe of N assets, investors often form equally weighted portfolios (EWPs) by selecting subsets of assets. EWPs are simple, robust, and competitive out-of-sample, yet the uncertainty about which subset truly performs best is largely ignored. Traditional approaches typically rely on a single selected portfolio, but this fails to consider alternative investment strategies that may perform just as well when accounting for statistical uncertainty. To address this selection uncertainty, we introduce the Selection Confidence Set (SCS) for EWPs: the set of all portfolios that, under a given loss function and at a specified confidence level, contains the unknown set of optimal portfolios under repeated sampling. The SCS quantifies selection uncertainty by identifying a range of plausible portfolios, challenging the idea of a uniquely optimal choice. Like a confidence set, its size reflects uncertainty -- growing with noisy or limited data, and shrinking as the sample size increases. Theoretically, we establish that the SCS covers the unknown optimal selection with high probability and characterize how its size grows with underlying uncertainty, corroborating these results through Monte Carlo experiments. Applications to the French 17-Industry Portfolios and Layer-1 cryptocurrencies underscore the importance of accounting for selection uncertainty when comparing equally weighted strategies.
We introduce multiscale topological analysis for studying cryptocurrency price series in the time domain. This is achieved by first performing a coarse-grained procedure on the volatility series at multiple temporal scales, and then constructing consecutive visibility graphs from the resulting coarse-grained series. We show that their degree distribution presents a likely power-law behavior. This scaling characteristics keeps invariant even varying time scale factor. Interestingly, we find that the number of cliques that capturing higher-order relations, presents a clear power-law behavior with the time scale factor. Their associated scaling exponent shows a monotonically decreasing pattern. Our work reveals the function of higher-order topological structure underlying cryptocurrency time series.
As the convergence of blockchain, cryptocurrency, and artificial intelligence technologies continue to evolve, they are merging to revolutionize decision-making processes, financial transactions, and strategic business operations across various industries. This chapter aims to explore the dynamics of this convergence, examining its potential to drive significant shifts in how businesses operate, manage risks, and create value in a rapidly digitizing global economy. The mission of this chapter is twofold- To provide an in-depth analysis of how the integration of AI and cryptocurrency is reshaping traditional business models and decision-making strategies and To identify practical business applications and strategic advantages that companies can harness by adopting these technologies. By delving into the synergies between AI and cryptocurrency, the chapter seeks to offer readers a comprehensive understanding of their transformative impact on businesses and guide organizations in leveraging these innovations for long-term competitive advantage.
Ketidakpastian ekonomi dan inflasi mendorong investor untuk mencari aset lindung nilai. Emas telah lama dianggap sebagai safe haven, sementara Bitcoin mulai dipandang sebagai alternatif digital. Penelitian ini bertujuan untuk menganalisis pengaruh harga emas terhadap pergerakan harga Bitcoin dengan indeks Dow Jones sebagai variabel kontrol. Metode yang digunakan adalah regresi linear dengan pendekatan Ordinary Least Squares (OLS), menggunakan data sekunder bulanan dari tahun 2019 hingga Oktober 2024. hasil penelitian menunjukkan bahwa emas memiliki pengaruh positif dan signifikan terhadap harga Bitcoin, sedangkan indeks Dow Jones tidak menunjukkan pengaruh yang signifikan. Temuan ini mendukung teori penyimpan nilai (store of value) dan semakin memperkuat peran Bitcoin sebagai aset alternatif dalam menghadapi ketidakpastian ekonomi. Hasil ini dapat memberikan implikasi praktis bagi investor dan pengambil keputusan dalam menggunakan pergerakan harga emas sebagai indikator untuk mengantisipasi pergerakan harga Bitcoin dan strategi manajemen risiko portofolio di tengah transisi global menuju digitalisasi ekonomi.