Counterfeit consumer products have become a serious global issue effecting several industries like medicines, electronics, luxury goods and fast moving consumer goods. Existing supply chain management systems offer no transparency and traceability creating vulnerabilities for counterfeit goods to access legitimate marketplaces. This paper proposes a complete blockchain-based framework for counterfeit goods detection and authentication. Our proposed framework uses Ethereum blockchain technology, smart contracts programmed in Solidity and a user-friendly ReactJS UI to create product authentication system that is immutable and transparent. The software allows producers to register authentic products with unique identifiers (UIDs) such as QR codes or serial numbers on the blockchain along with product metadata such as manufacturer, manufacture date, batch data, and product specifications. Consumers and vendors can utilize the UI to authenticate product claims for trust verification. As consumer and vendor users are using the UI, they can even verify authenticity with the real-time capability to interact with the blockchain, while also ensuring data integrity through user tampering and illegal changes. The UI is utilizing the Truffle framework for creating and deploying a smart contract on the Ethereum blockchain, Ganache local blockchain simulator, and web3.js for being able to connect from frontend to the blockchain. Experimental results show a significant improvement in authenticating goods, decreased verification time and increased transparency through the use of our product authentication system. The proposed framework represents a viable solution to significant challenges in product authentication while providing scalability, security and cost effectiveness for global acceptance and use.
The Personal Portfolio serves as the basis for professional representation, as it holds the most weight. However, these centralized Portfolio solutions can be susceptible to such issues as data tampering, security breaches, system downtime, and non-verifiable validity. This article will introduce Decentralize Portfolio, a blockchain-enabled portfolio management solution that utilizes a decentralized architecture to offer Security, Integrity, and Transparency. This solution provides real ownership and trustless verification through the use of IPFS for the storage of files, Ethereum Smart Contracts to generate immutably stored hash codes, and MetaMask for secure user-controlled access. A discussion on the design, development, and potential impact of the Decentralized Portfolio on web3-based Professional Identities will be provided.
ABSTRACT The combination of blockchain technology with Industrial Internet of Things (IIoT) frameworks is promising in terms of building trust, data authenticity, and resilience. However, the efficiency and feasibility of integration largely rely upon the consensus mechanisms used. The present study is an overview of four renowned blockchain consensus schemes, namely Proof of Work (PoW), Proof of Stake (PoS), Practical Byzantine Fault Tolerance (PBFT), and Delegated Proof of Stake (DPoS), and the corresponding performance, security, efficiency, as well as compatibility under IIoT. The results reveal that low‐latency and lightweight consensus, such as PBFT and DPoS, will be helpful in IIoT applications, especially in applications with scarce resources. The paper offers practical guidance on the development of IIoT systems with integrated blockchain customized based on the requirements of the industry.
The rise of Decentralized Finance (DeFi), enabled by blockchain technology, has introduced open and transparent financial ecosystems that contrast sharply with traditional financial systems. While DeFi expands the financial landscape and democratizes participation in global markets, it also introduces new complexities. Classic valuation models used in traditional finance often fall short in this context. However, DeFis transparency---where all transactions are publicly recorded---offers a unique opportunity to model and understand market behavior using modern analytical tools. Motivated by the challenges and opportunities of DeFi, the main goal of this thesis is to propose novel methods to understand market dynamics through the lens of network science and machine learning. To this end, we focus on four specific objectives: (i) assess whether structural information from blockchain transaction networks provides predictive signals beyond traditional indicators; (ii) develop robust trust-based valuation metrics for DeFi protocols; (iii) develop a framework for forecasting financial time series through uncertainty-aware machine learning architectures; (iv) construct diversified portfolios using network-based representations of asset relationships. First, using Ethereum as a case study, we analyze the influence of the transaction network on market trends by comparing the performance of two machine learning models: one that uses technical analysis and social media indicators commonly found in the literature and another that incorporates structural properties of the transaction network. We found that by including transaction network information, we can anticipate 46% more uptrends and 19% more downtrends, highlighting the predictive power of the transaction network. Second, we introduce the TVL/MCAP bands as a tool to identify periods of overconfidence and underconfidence in the DeFi market. We show that extreme values of this indicator can signal price movements: values above the 95th percentile are associated with a 15\% higher return in the following month, while values below the 5th percentile anticipate declines, highlighting investor confidence as a key market driver. Third, we address the need for forecasts that not only anticipate market trends but also quantify the uncertainty surrounding them. To this end, we integrate Reservoir Computing (RC) with conformal prediction methods to provide statistically rigorous forecasts along with prediction intervals. We found that RC outperform traditional econometric models, particularly in anticipating the trend of financial time series. Furthermore, we show that conformal methods, especially quantile-conformal variants, significantly improve forecast reliability while adapting to market volatility. Finally, we address the challenge of portfolio optimization using network-based methods. Specifically, we model the network of relationships between cryptocurrencies to obtain a market representation that enables selecting a more diversified portfolio. We find that peripheral assets enhance portfolio stability and returns, while links bridging network communities carry higher risk. Thereby, these results highlight the importance of structural diversification in volatile markets. In addition, we contribute to refining pairs trading strategies by proposing the Hurst exponent to identify rapid mean-reversion opportunities. We show that anti-persistent values of H lead to faster reversion and consistent returns---minimizing trading costs and enabling broader portfolio construction. In conclusion, this thesis provides an interdisciplinary analytical framework that advances our understanding of DeFi markets. By introducing network-based indicators, trust metrics, uncertainty-aware forecasts, and diversification strategies grounded in market structure, we provide new tools for investors and researchers to navigate the complexity and volatility inherent in decentralized financial systems. RESUMEN El auge de las Finanzas Descentralizadas (DeFi), impulsado por la tecnología blockchain, ha dado lugar a ecosistemas financieros más accesibles y transparentes que contrastan con los sistemas financieros tradicionales. DeFi amplía el panorama financiero actual e introduce nuevos retos, como la necesidad de un nuevo modelo de valoración de los activos. No obstante, el hecho de que todas las transacciones son públicas, ofrece una oportunidad única para modelar y comprender la dinámica del mercado mediante nuevas herramientas analíticas. Esta tesis tiene como objetivo principal proponer nuevos métodos para comprender la dinámica del mercado desde la perspectiva de los sistemas complejos y el aprendizaje automático. Para ello, nos centramos en cuatro objetivos específicos: (i) evaluar si la información estructural de las redes de transacciones aporta señales predictivas más allá de los indicadores tradicionales; (ii) desarrollar métricas de valoración de los protocolos DeFi basadas en la confianza de los inversores; (iii) construir un marco metodológico para predecir series temporales financieras mediante arquitecturas de aprendizaje automático que incorporen incertidumbre; (iv) construir portfolios diversificados utilizando representaciones de la red de relaciones entre criptomonedas. En primer lugar, utilizando Ethereum como caso de estudio, analizamos la influencia de la red de transacciones sobre la tendencia del mercado comparando dos modelos de aprendizaje automático: uno que emplea indicadores de análisis técnico y de redes sociales comunes en la literatura, y otro incluyendo propiedades estructurales de la red de transacciones. Los resultados muestran que incluyendo información de la red podemos anticipar un 46% más de tendencias alcistas y un 19% más de tendencias bajistas, lo que subraya el poder predictivo de la red de transacciones. En segundo lugar, introducimos las bandas TVL/MCAP para identificar períodos de sobreconfianza y desconfianza en el mercado DeFi. Demostramos que valores extremos de este indicador anticipan movimientos en el precio: valores por encima del percentil 95 se asocian con un rendimiento 15% superior en el mes siguiente, mientras que valores por debajo del percentil 5 anticipan caídas. En tercer lugar, abordamos la necesidad de predicciones que no solo anticipen tendencias del mercado, sino que también cuantifiquen la incertidumbre. Para ello, integramos Reservoir Computing (RC) con métodos de predicción conforme para generar predicciones estadísticamente rigurosas junto con intervalos de confianza. Mostramos que RC supera a los modelos econométricos tradicionales, especialmente anticipando la tendencia del precio. Además, los métodos conformes ---en particular las variantes de cuantiles--- mejoran significativamente la fiabilidad de las predicciones al adaptarse a la volatilidad del mercado. Por último, abordamos el problema de optimización de portfolios mediante métodos basados en redes. Específicamente, modelamos la red de relaciones entre criptomonedas para seleccionar un portfolio más diversificado. Observamos que evitar pares que conectan distintas comunidades en la red y priorizar activos periféricos aumenta el rendimiento y disminuye el riesgo, demostrando así la importancia de una diversificación estructural. Además, proponemos el uso del exponente de Hurst para identificar oportunidades que revierten antes a la media en estrategias de pairs trading. En conclusión, esta tesis propone un marco analítico interdisciplinar que contribuye al entendimiento de los mercados DeFi. Al introducir indicadores basados en redes, métricas de confianza, predicciones con estimación de incertidumbre y estrategias de diversificación basadas en la estructura del mercado, ofrecemos nuevas herramientas para que inversores e investigadores naveguen la complejidad y volatilidad propias de los sistemas financieros descentralizados.
Deepak Gupta, K. R. Shobha, D. Hariprasad, Rakhi Chawla · 7 authors
Digital Public Infrastructure (DPI) represents a transformative paradigm for emerging economies, providing foundational systems for identity verification, payment processing, and data exchange. The convergence of DPI with Web3 technologies and decentralized infrastructure creates unprecedented opportunities for inclusive economic development. This chapter examines how blockchain, distributed ledger technologies, and decentralized protocols enhance traditional DPI frameworks, analyzing implementations across emerging markets with emphasis on India's pioneering India Stack model. Through examination of 220 million active blockchain addresses globally and DPI implementations reaching 1.3 billion citizens, this research demonstrates that Web3-enabled DPI can overcome institutional voids, reduce transaction costs by up to 70%, and facilitate financial inclusion for 730 million unbanked adults. The chapter analyzes technical architectures, governance models, security frameworks, and socioeconomic impacts while addressing challenges including digital divides, regulatory gaps, and sustainability concerns. Key findings reveal that DPI integrated with Web3 infrastructure contributed 0.9% to GDP in 2022, projected to reach 4.2% by 2030, with the global blockchain market valued at $31.18 billion in 2025 and forecasted to reach $393.42 billion by 2032.
Taki E. M. Abedesselam, Fabio Giacomelli, Francesco Pasquale, Michele Salvi
We study a random process over graphs inspired by the way payments are executed in the Lightning Network, the main layer-two solution on top of Bitcoin. We first prove almost tight upper and lower bounds on the time it takes for a payment failure to occur, as a function of the number of nodes and the edge capacities, when the underlying graph is complete. Then, we show how such a random process is related to the edge-betweenness centrality measure and we prove upper and lower bounds for arbitrary graphs as a function of edge-betweenness and capacity. Finally, we validate our theoretical results by running extensive simulations over some classes of graphs, including snapshots of the real Lightning Network.
The maintenance of rail vehicles and infrastructure plays a critical role in reducing delays, preventing malfunctions, and ensuring the economic efficiency of rail transportation companies. Predictive maintenance systems powered by supervised machine learning offer a promising approach by detecting failures before they occur, reducing unscheduled downtime, and improving operational efficiency. However, the success of such systems depends on high quality labeled data, necessitating user centered labeling interfaces tailored to annotators needs for Usability and User Experience. This study introduces a cost effective predictive maintenance system developed in the federally funded project DigiOnTrack, which combines structure borne noise measurement with supervised learning to provide monitoring and maintenance recommendations for rail vehicles and infrastructure in rural Germany. The system integrates wireless sensor networks, distributed ledger technology for secure data transfer, and a dockerized container infrastructure hosting the labeling interface and dashboard. Train drivers and workshop foremen labeled faults on infrastructure and vehicles to ensure accurate recommendations. The Usability and User Experience evaluation showed that the locomotive drivers interface achieved Excellent Usability, while the workshop foremans interface was rated as Good. These results highlight the systems potential for integration into daily workflows, particularly in labeling efficiency. However, areas such as Perspicuity require further optimization for more data intensive scenarios. The findings offer insights into the design of predictive maintenance systems and labeling interfaces, providing a foundation for future guidelines in Industry 4.0 applications, particularly in rail transportation.
Andrea Venturi, Imanol Jerico-Yoldi, Francesco Zola, Raul Orduna
As Law Enforcement Agencies advance in cryptocurrency forensics, criminal actors aiming to conceal illicit fund movements increasingly turn to "mixin" services or privacy-based cryptocurrencies. Monero stands out as a leading choice due to its strong privacy preserving and untraceability properties, making conventional blockchain analysis ineffective. Understanding the behavior and operational patterns of criminal actors within Monero is therefore challenging and it is essential to support future investigative strategies and disrupt illicit activities. In this work, we propose a case study in which we leverage a novel graph-based methodology to extract structural and temporal patterns from Monero transactions linked to already discovered criminal activities. By building Address-Ring-Transaction graphs from flagged transactions, we extract structural and temporal features and use them to train Machine Learning models capable of detecting similar behavioral patterns that could highlight criminal modus operandi. This represents a first partial step toward developing analytical tools that support investigative efforts in privacy-preserving blockchain ecosystems
The rapid growth of quantum computing poses a threat to the cryptographic foundations of digital systems, requiring the development of secure and scalable electronic voting (evoting) frameworks. We introduce a post-quantum-secure evoting architecture that integrates Falcon lattice-based digital signatures, biometric authentication via MobileNetV3 and AdaFace, and a permissioned blockchain for tamper-proof vote storage. Voter registration involves capturing facial embeddings, which are digitally signed using Falcon and stored on-chain to ensure integrity and non-repudiation. During voting, real-time biometric verification is performed using anti-spoofing techniques and cosine-similarity matching. The system demonstrates low latency and robust spoof detection, monitored through Prometheus and Grafana for real-time auditing. The average classification error rates (ACER) are below 3.5% on the CelebA Spoof dataset and under 8.2% on the Wild Face Anti-Spoofing (WFAS) dataset. Blockchain anchoring incurs minimal gas overhead, approximately 3.3% for registration and 0.15% for voting, supporting system efficiency, auditability, and transparency. The experimental results confirm the system's scalability, efficiency, and resilience under concurrent loads. This approach offers a unified solution to address key challenges in voter authentication, data integrity, and quantum-resilient security for digital systems.
Jianting Zhang, Sen Yang, Alberto Sonnino, Sebastián Loza · 5 authors
Directed Acyclic Graph (DAG)-based Byzantine Fault-Tolerant (BFT) protocols have emerged as promising solutions for high-throughput blockchains. By decoupling data dissemination from transaction ordering and constructing a well-connected DAG in the mempool, these protocols enable zero-message ordering and implicit view changes. However, we identify a fundamental liveness vulnerability: an adversary can trigger mempool explosions to prevent transaction commitment, ultimately compromising the protocol's liveness. In response, this work presents Lifefin, a generic and self-stabilizing protocol designed to integrate seamlessly with existing DAG-based BFT protocols and circumvent such vulnerabilities. Lifefin leverages the Agreement on Common Subset (ACS) mechanism, allowing nodes to escape mempool explosions by committing transactions with bounded resource usage even in adverse conditions. As a result, Lifefin imposes (almost) zero overhead in typical cases while effectively eliminating liveness vulnerabilities. To demonstrate the effectiveness of Lifefin, we integrate it into two state-of-the-art DAG-based BFT protocols, Sailfish and Mysticeti, resulting in two enhanced variants: Sailfish-Lifefin and Mysticeti-Lifefin. We implement these variants and compare them with the original Sailfish and Mysticeti systems. Our evaluation demonstrates that Lifefin achieves comparable transaction throughput while introducing only minimal additional latency to resist similar attacks.
Academic certificate fraud has become a significant concern for universities, institutions, and employers worldwide, as it directly affects the credibility and reliability of academic qualifications. Conventional methods for verifying certificates are largely manual, tedious, and prone to errors or manipulation, as they lack a centralized and tamper-resistant validation mechanism. The emergence of blockchain technology offers a revolutionary solution by enabling decentralized and immutable storage of certificate data, ensuring trust and transparency. This paper proposes and implements a blockchain-based academic certificate authentication framework that utilizes Ethereum smart contracts to securely record and manage certificate metadata. Additionally, the InterPlanetary File System (IPFS) is integrated to facilitate decentralized and permanent storage of certificate files. The proposed system ensures that certificates are verified transparently, instantly, and without reliance on third-party intermediaries, thus streamlining the overall verification process. It empowers academic institutions to issue certificates securely while enabling verifiers to authenticate them efficiently, reducing administrative burden and minimizing the risk of document forgery. The results of the implementation demonstrate superior data integrity, operational performance, and user trust when compared to traditional verification approaches. This paper presents the existing challenges in certificate validation, details the methodology and design of the system, and provides experimental evaluations to establish its effectiveness for secure academic certificate verification.
The article summarizes trends and cases from the last five years pertaining to the adoption of block chain technology in public government, the financial industry, and associated infrastructures (voting, energy, supply chains, and identities). Examples of economic sectors where the application of block chain technology has been successful are presented. It makes a distinction between centrally issued digital currencies and decentralized crypto-assets, examines the sustainability and energy efficiency of public block chain, and considers the suitability of immutable registries for official data and election procedures. It provides guidelines for creating a financial-monetary equivalent that would associate physical assets and energy with a distributed ledger value unit. The conclusion is that while block chain is still being institutionalized in certain sectors (traceability, settlement), its usage in politics (voting, identification) remains pilot-stage and context-dependent.
Security, Politics, and Digital Transformation
Economic, Social, and Public Health Issues in Russia and Globally
Blockchain smart contracts have been a groundbreaking technology yet are still prone to numerous security vulnerabilities that can lead to large financial and operational losses. In our prior work, we presented an in-depth methodology for data preprocessing and dataset preparation towards facilitating effective vulnerability detection in smart contracts. In this paper, building on that work, we proceed with our work by presenting a new hybrid deep learning architecture that integrates Graph Neural Networks (GNN) and CodeBERT in an efficient way to capture both structural and semantic code features. The hybrid model processes parallel representations of smart contracts: the GNN extracts graph-based control and data flow dependency relations, and CodeBERT makes use of pretrained contextual source code token embeddings. The two embeddings are concatenated and then fed into a shared classifier to predict the existence and types of vulnerabilities. We test our approach on a diverse collection of smart contracts and compare it with single-model baselines. Our hybrid model outperforms individual GNN and CodeBERT approaches with significant performance gains in precision, recall, and F1-score for different types of vulnerabilities. These findings confirm the effectiveness of our fusion approach and introduce the possibility of employing hybrid deep learning models in real-world smart contract security auditing.
Gensyn’s Verde Protocol: Technical Analysis of Decentralized ML Compute Verification is a 103-page technical deep dive into one of the most significant emerging architectures for decentralized machine learning. This paper provides a comprehensive examination of Gensyn’s Verde verification protocol, its refereed-delegation design, graph-based pinpointing system, probabilistic proof-of-learning mechanisms, and the RepOps reproducible operators framework. It analyzes the GHOSTLY problems Generalizability, Heterogeneity, Overhead, Scalability, Trustlessness, and Latency and evaluates how Verde addresses core limitations in verifying distributed ML training across heterogeneous hardware. By combining economic incentives, cryptographic commitments, and deterministic computation layers, this work outlines a practical blueprint for trustless, large-scale distributed AI training. The paper positions Gensyn within the broader ecosystem of Truebit, optimistic rollups, zero-knowledge systems, and decentralized compute networks, while highlighting open research questions and future directions. This publication aims to contribute a rigorous technical foundation for the democratization of AI infrastructure and the emergence of a global, permissionless compute marketplace.
Cryptocurrency, despite the blurred boundaries of domestic regulation, has firmly entered judicial practice as a type of property that can be seized or confiscated. The forces of criminal law are deterring such a new type of digital crimes as crypto crimes. The author analyzes judicial practice and the evidence base presented within the framework of the capabilities available to Russian law enforcement agencies. The priority directions of improving the process of proving illegal acts are substantiated on the basis of the existing provisions of the law on the sufficiency of evidence.
Ahmed Anwer Jaafa, Madhu Sahu, M. Jasmin, Mamadjanova Zukhra Bakhromjanovna · 8 authors
Sharing patient data safely and efficiently is still hard in the constantly changing world of digital healthcare because of worries about privacy, giving consent, and how different systems work together. This paper suggests using ChainMedX, which relies on blockchain technology to let patients, doctors, and other healthcare professionals exchange data in real time with dynamic consent consent management. ChainMedX uses permissions, smart contracts, and zero-knowledge proofs to let patients pick who can have access to their medical records, manage exactly what is shared, and specify the time period the sharing is needed, making sure those permissions cannot be altered. Being distributed across both cloud and edge servers, the patient-managed encrypted data vaults make active updates of medical records possible, complying with FHIR standards. Thanks to an AI-based consent suggestion module, patients receive useful advice that suits their needs and the current emergency situation. In addition, ChainMedX deals with urgent issues, such as fast access with easy ‘break-glass’ rules and complete tracking of every transaction, and makes it easier for healthcare services to interact with the wider health organization and verify insurance policies. According to the results, latency, security, and managing consent are all better in the new system than in systems that operate centrally or rely on blockchain. The research mentions that connecting blockchain, edge computing, and privacy-based cryptography can form a healthcare system that respects patient data privacy and makes healthcare cooperation speedy and secure. The goal of this framework is to help provide for data exchange between countries, while including new forms of digital health technology, all this aims to strengthen patient trust and the integrity of their medical data, along with providing better healthcare outcomes.
A big and always-changing network of nodes that are connected to each other is what makes Bitcoin and other decentralized block chain networks safe, scalable, and reliable. It takes a lot of time and work to accurately map and analyses these large-scale topologies because of their communication overhead and inherent complexity. We introduce a novel topology discovery methodology that addresses this issue by integrating lightweight “probe” nodes for quick data collection, a clustering mechanism to aggregate stable nodes for enhanced accuracy, and a visualization system capable of displaying the network's layered structure in realtime. Our tests on the Bitcoin network demonstrate that the suggested solution gets 95 % of the mapping right and cuts down on communication cost by roughly 72 %. The framework's architecture is not particular to Bitcoin; instead, the methodology can be used to various blockchain networks with similar peer-to-peer topologies. It may be used by researchers, developers, and system administrators to evaluate, monitor, and improve distributed ledger systems because it is scalable and works well. Its generalizability ensures that it can affect several blockchain ecosystems.
S. Yadav, Jyoshitha Pechetti, G Tarunya, Meena Belwal
The adoption of Electronic voting systems as alternative method suits distributed digital infrastructures since they provide promising voting solutions. The protection of data integrity alongside complete transparency of elections together with assured voter privacy and system defense represent ongoing technical obstacles. A new blockchain-powered electronic voting platform is introduced for running multiple voting levels at both general elections and state elections through four candidates per election. A distributed ledger system enables permanent vote recording while promoting tamper-evidence through auditing capabilities. The system achieves voter authentication through an encrypted HS256 password mechanism which protects users from unauthorized entry and protects password information from theft. Smart contracts process votes which then get attached to the blockchain ledger to ensure transparency and make challenges to vote results impossible. The proposed system operates with distributed architecture to eliminate network threats and develop trust between all system stakeholders. The system passes simulation results because it shows exceptional resistance against security threats while maintaining reliability and scalability for digital national elections.
Provable security is a cornerstone of modern cryptography: Due to ubiquitous and diverse applications of cryptography, a proof of security gives us the necessary confidence to deploy a cryptographic protocol. In most cases, such a security proof comes in the form of a black-box reduction, which bases the security of a potentially complex protocol on a small set of simple and abstract assumptions that are much easier to analyse. However, proving a black-box reduction can be quite complicated, and we do not have proofs for every protocol used in practice. Here, analysing the protocols relative to oracles, a technique from computational complexity theory, can provide insights: Oracles provide the ability to compute functionalities in one computational step that otherwise might not be efficiently computable, e.g., provide access to a truly random function or solve any NP-complete problem. These oracles now allow us to replace some parts in the protocol with abstract, idealized primitives that are easier to analyse, e.g., to replace a one-way function with a truly random function. In this thesis, we utilize oracles in two different ways. In the first part, we use oracles to prove lower bounds for cryptographic primitives, i.e., showing that certain assumptions are not sufficient to build this primitive securely. The essential idea here, going back to Impagliazzo and Rudich, is to replace the assumption with an oracle, i.e., replacing a one-way function with a truly random function, and then showing that relative to this oracle, it is impossible to build the primitive. From this impossibility result relative to the oracle, we can now conclude that the primitive cannot be built from the assumption in a black-box way. We use this technique to prove a lower bound on the efficiency of constructing strong from weak one-way functions, to show that we cannot construct collision-resistant hash functions from distributional collision-resistant hash functions in a fully black-box way, and to prove that extremely lossy functions cannot be built from a large class of symmetric primitives in a black-box way. In the second part of this thesis, we use oracles as idealized models that can be used to provide heuristic security arguments for protocols.These idealized models, starting with the random oracle model (short ROM) introduced and defined by Fiat and Shamir as well as Bellare and Rogaway, were motivated by the existence of very efficient cryptographic protocols used in practice, but for which no proof of security existed. Using idealized models, it was now possible to give at least a heuristic security argument for them. In this thesis, we first focus on the common random string model, an idealized model introduced to circumvent impossibility results for non-interactive zero-knowledge proofs. We show how to reuse a single common random string for polynomially many non-interactive statistical zero-knowledge arguments, as well as analyze the relation between different soundness definitions used in literature. In a second result, we introduce an alternative notion for the ROM, the universal random oracle model, which brings this idealized model closer to reality.
Standard electrodynamics relies on two free-space parameters, vacuum permittivity ($\epsilon_0$) and vacuum permeability ($\mu_0$), to govern the speed of light. These constants act as scalar correction factors without providing geometric insight into the fabric of space. This paper demonstrates that in the Quantum Measurement Units (QMU) system, these abstract constants are replaced by a single geometric ledger governed by the Aether unit ($A_u$) and the curl unit ($\mathrm{curl}$). We show that the Maxwell wave equation resolves naturally into the Aether's rotational and torsional limits, where the propagation velocity is exactly the product of the quantum frequency ($F_q$) and the Compton wavelength ($\lambda_C$). Furthermore, we derive the Impedance of Free Space ($Z_0$) as a direct function of the QMU conductance unit ($\mathrm{cond}$), proving that vacuum impedance is the geometric ratio of magnetic flux density to distributed charge: $$Z_0 = \frac{1}{2\alpha \cdot \mathrm{cond}}$$ This derivation removes the need for arbitrary free-space constants, reducing the Maxwell equations to a closed geometric identity perfectly consistent with experimental data.
M Dhinesh, A. Karthik, Abdur Rahim M, S. AARYA · 6 authors
The rapid expansion of India's e-commerce ecosystem has led to a corresponding rise in consumer grievances, data privacy violations, and non-compliance with statutory norms. Although the Consumer Protection Act, 2019 and the Consumer Protection (E-Commerce) Rules, 2020 mandate transparent disclosures, grievance redressal mechanisms, and seller accountability, enforcement remains inconsistent due to the centralized nature of compliance systems. This paper outlines a proposal of a Blockchain-Driven Compliance Model (BDCM) based on permissioned blockchain infrastructure, smart contracts, and zero-knowledge proofs to guarantee automated, auditable, and enforceable legal compliance. The architecture has the main legal points in smart contracts, including Rule$4(4)$on product disclosure, Rule 5(3) on record retention, and Rule 6(3) on seller liability, and a compliance scoring/alerts system to dynamically monitor the trust is provided. The outputs of simulations on Hyperledger Fabric show that the compliance will be substantially enforced. Most legal clauses had a success rate of$\geqslant 97.5$and grievance redressal time was also lowered by 75 and consumer satisfaction increased to 94.7. The over-95% privacy index trust index guaranteed privacy of the model through the use of the zero-knowledge consent verification algorithm to guarantee privacy of the model among the users who had tested the model. Moreover, the compliance scores successfully ranked sellers according to their legal conduct allowing a proactive suspension and warning of the potential high risk entities. To sum up, the BDCM framework provides a legal-tech interface between legal requirements and technical implementation, which can be transparent, auditable, and trusted.
Mohhammed H. Al-Farouni, Jyotsna Dwivedi, T. Saravanan, Ismatullaeva Yodgora Abduvahobkizi · 8 authors
Online elections (e-voting) are fast and convenient. Still, there is growing concern that the democratic integrity of the election process is under threat due to problems such as cyberattacks, illegal intrusion, vote manipulation, and unclear verification of the results. Actual cases have demonstrated voter fraud, insecure data storage, and unreliable results, undermining the public's confidence in digital voting systems, particularly in primary national elections. Moreover, the traditional auditing system, which relies on paper ballots, manual logistics, and resource-intensive verification, results insignificant operational costs and a negative environmental impact. This paper proposes a solution to these capital challenges by introducing SECURE-VOTE_CHAIN, an open, secure e-voting platform that integrates a certified blockchain network, biometric verification checks, homomorphic encryption, and zero-knowledge public audit records. The blockchain nodes in this system, run by voting bodies and legitimate observers, consistently registered votes and facilitated decentralised consultation. Biometric authentication can exclude identity duplication and voter fraud, and only homomorphic encryption can ensure fair counting without knowing any votes provided by an individual. Zero-knowledge proofs also make public auditability achievable without reducing the anonymity of voters. The application of realistic election parameters in simulations yields an accuracy of 91.88, along with low confirmation latency and high attack resilience probabilities in the presence of insider attacks, replay attacks, and denial-of-service attempts. In addition to security and reliability, the system will eliminate paperwork and manual auditing, significantly reducing the carbon footprint of traditional elections. Altogether, SECURE-VOTE_CHAIN offers an environmentally friendly, secure, and scalable solution that is suitable for regaining voter confidence, ensuring electoral integrity, and creating a future-proof digital governance model. The model can be considered a reasonably helpful standard by which contemporary voting systems operate, as it combines technological benefits with the adequacy of achievements in terms of prospects, providing policymakers with dependable means of security and transparency in election procedures, applicable to both urban and rural settings.
Revocable ring signatures protect signer anonymity. A trusted authority can revoke signing rights when necessary. This makes them attractive for blockchains and vehicular networks. Existing lattice-based ring signature schemes are only traceable. They can de-anonymize a malicious signer, yet fail to stop the revoked signer from creating a valid signature. This contradicts the very notion of revocation. We introduce a polynomial-based revocation list. It is enforced with non-interactive zero-knowledge proofs of knowledge. Our protocol implicitly verifies the up-to-date revocation list during signature generation. Consequently, revoked signers cannot authenticate and are effectively excluded. Integrating this mechanism into a lattice setting, we obtain a compact revocable ring signature. The scheme is correct, anonymous, unforgeable, and truly revocable under the random oracle model. No costly key updates are required. Compared with prior trace-and-update schemes, our construction offers a practical post-quantum primitive. It guarantees both privacy and accountable revocation. Overhead analysis shows that we have added very little cost while ensuring true revocation.