Public blockchains impose an inherent tension between regulatory compliance and user privacy. Existing on-chain identity solutions require centralized KYC attestors, specialized hardware, or Decentralized Identifier (DID) frameworks needing entirely new credential infrastructure. Meanwhile, over four billion active X.509 certificates constitute a globally deployed, government-grade trust infrastructure largely unexploited for decentralized identity. This paper presents zk-X509, a privacy-preserving identity system bridging legacy Public Key Infrastructure (PKI) with public ledgers via a RISC-V zero-knowledge virtual machine (zkVM). Users prove ownership of standard X.509 certificates without revealing private keys or personal identifiers. Crucially, the private key never enters the ZK circuit; ownership is proven via OS keychain signature delegation (macOS Security.framework, Windows CNG). The circuit verifies certificate chain validity, temporal validity, key ownership, trustless CRL revocation, blockchain address binding, and Sybil-resistant nullifier generation. It commits 13 public values, including a Certificate Authority (CA) Merkle root hiding the issuing CA, and four selective disclosure hashes. We formalize eight security properties under a Dolev-Yao adversary with game-based definitions and reductions to sEUF-CMA, SHA-256 collision resistance, and ZK soundness. Evaluated on the SP1 zkVM, the system achieves 11.8M cycles for ECDSA P-256 (17.4M for RSA-2048), with on-chain Groth16 verification costing ~300K gas. By leveraging certificates deployed at scale across jurisdictions, zk-X509 enables adoption without new trust establishment, complementing emerging DID-based systems.
Muruganantham Angamuthu, Mohammad Kanan, M Yasaswini, M. Silambaeasan · 6 authors
Voting by paper casts doubt on democratic processes due to security flaws, fraud, opaqueness, and limited verifiability. People want voting methods that are trustworthy and that withstand the digital revolution. This piece takes a look at a more effective voting mechanism that uses blockchain technology. By using the immutability, cryptographic resilience, and decentralization of DLT, this technology generates secure and verifiable elections. The foundation of a contemporary end-to-end voting system is digital identity management, cryptography that preserves anonymity, and mechanisms for reaching a consensus. Secure voting records are safeguarded from tampering and fraud by means of the distributed ledger technology known as blockchain. With the help of smart contracts, human error and manipulation may be eliminated from the voting process by completely automating voter verification, ballot validation, and vote tallying. While keeping voters’ identities secure, homomorphic encryption and zero-knowledge proofs (ZKPs) confirm and monitor results. The security and efficiency of voter registration are enhanced by biometric identification verification and multi-factor authentication. Data collecting, voter verification, distributed validation, secure ballot casting, and open auditing of outcomes are all components of hierarchical design, as per the research. Hybrid blockchains combine public and permissioned ledgers to provide scalable and transparent election monitoring. Blockchain adoption is hindered by energy consumption, usability, and latency difficulties. These problems can be solved using efficient data structures and lightweight consensus algorithms.
Konduri S P S Narayana Murthy, Tirumala Anand Kumar, R.Nivedha, B Balasaigayathri
In this paper, I introduce a decentralized freelancing site that combines blockchain-based smart-contract escrow with AI-based matching of talents to improve the level of trust, transparency, and efficiency in digital labor markets. Conventional freelancing models make use of centralized middlemen, which introduce vulnerability to the system in the form of payment conflicts, recommendations of jobs, manipulation of data and single point failure. The suggested system will resolve these concerns by implementing an unchanging Ethereum smart-contract escrow, which automates the processes of hiring, funds locking, and milestone payments without the involvement of third parties. The hybrid backend application with FastAPI, the SQLite data store, and the Web3 interaction provides the security of the requests processing and the state of the contract retrieval in real-time. Also, there is an AI ranking module with TF-IDF vectorization and cosine similarity that offers personalized matching of freelancers and jobs based on skill-relevant and experience characteristics. An up-to-date React/Tailwind frontend provides an easy user flow of posting jobs, hiring freelancers, depositing escrow funds, and approving task completion. Experimental analysis using live contract deployment shows that the transparency of transactions is more optimal, the probability of dispute is lower, and the accuracy of the matching is much higher. This site is an example of how the next-generation decentralized freelance ecosystems can be practiced.
Framed by research as part of the ongoing project ‘Archiving Community: Social Infrastructure and Small-Scale, Online Radio Stations’ (University of Glasgow, National Library of Scotland and University of Westminster), this 15 minute lecture-performance adopted a creative sound-essay format, spoken live over a soundscape developed collaboratively with artist Excel DJ. Using the name of the record label GLARC (Greater Lanarkshire Auricular Research Council) as a starting point, this talk considered ‘institutional’ constructs and the imaginaries surrounding permanence, location and the infrastructure surrounding music labels and libraries. Spanning texts on community archiving, the digital commons and considering changing ideas around participation, access and listening (particularly around web2/web3), it brought together diverse and sometimes contradictory ideas on time, space and the archive, exploring how institutions are formed, and the peculiar feedback-loops involved with copying, collecting and circulating iterative DIY ecosystems.
Semaria Eva Elita Girsang, Shaumiwaty, Muhammad Noval Aryansah, Mario Putra Sanjaya · 5 authors
The development of blockchain technology has driven the emergence of Non Fungible Tokens (NFTs) as unique digital assets traded through specialized marketplaces, forming a new digital economic ecosystem. Despite the rapid growth of the NFTs market, issues such as price volatility, the dominance of speculative activities, and uncertainty regarding long-term economic value remain insufficiently understood in academic studies. This research aims to analyze the role of NFTs marketplaces in shaping the economic value of digital assets, identify the factors influencing NFTs price dynamics, and evaluate the economic implications of the NFTs market for creators, investors, and marketplace platforms. This study employs an empirical quantitative approach by utilizing NFTs transaction data obtained from the OpenSea API, NonFungible.com, and CryptoSlam. The variables analyzed include NFTs prices, trading volume, liquidity, creator reputation, rarity score, and asset category. Data analysis is conducted using statistical and econometric methods to identify price determinants and market dynamics. The results indicate that NFTs values are significantly influenced by scarcity levels, creator reputation, asset utility, and the visibility provided by marketplaces. Marketplaces play a crucial role in shaping liquidity and market expectations, but they also contribute to increased volatility and speculative tendencies. This study concludes that the NFTs market has the potential to generate real economic value, yet it continues to face risks related to speculation and instability. These findings contribute theoretically to the digital economics literature and provide practical implications for the development of a more sustainable NFTs ecosystem.
Storage scalability is paramount in the era of big data blockchain. A storage-scalable blockchain can effectively scale out state storage to an arbitrary number of nodes and reduce the storage pressure on each, similar to distributed databases. Prior research has extensively utilized sharding techniques to attain storage scalability; however, these approaches invariably compromise safety and liveness guarantees. In this work, we propose a novel state-execution decoupled architecture, and Supermassive Blockchain, a novel storage-scalable Byzantine fault tolerance (BFT) protocol that can sustain the deterministic security properties of conventional BFT protocols. The state management system employs erasure coding to ensure state availability with scalable storage consumption, while the global consensus and execution layers maintain robust security characteristics. Our evaluation indicates that Supermassive Blockchain achieves better storage scalability compared to prior approaches while incurring low network overhead.
Solana has experienced rapid growth due to its high performance and low transaction costs, but the extremely low barrier to token issuance has also enabled widespread Rug Pulls. Unlike Ethereum-based Rug Pulls, which often rely on malicious smart-contract logic, Solana's unified SPL Token program shifts fraudulent execution toward on-chain behavioral manipulation. However, existing research has not systematically examined these Solana-specific Rug Pull patterns, and no public Solana Rug Pull dataset is available for empirical research. To bridge this gap, we present a large-scale measurement study of Rug Pulls on Solana. We manually verify 68 community-reported incidents and curate a benchmark of 117 confirmed Rug Pull tokens, from which we distill three representative on-chain behavioral patterns: Freeze Authority Abuse, Liquidity Withdrawal, and Pump-and-Dump. Guided by these patterns, we design a behavior-guided candidate identification and human-validation pipeline. We apply this pipeline to 100,063 tokens newly issued on Orca, Raydium, and Meteora during the first half of 2025, identifying 76,469 Rug Pull tokens. A random manual audit of 382 samples estimates a labeling false-positive rate of 0.26\%, supporting the reliability of the dataset. We release the resulting dataset and use it to characterize the Solana Rug Pull ecosystem. Our analysis shows that Rug Pulls on Solana exhibit extremely short lifecycles, strong price-driven dynamics, severe economic losses, and highly organized group behaviors. These findings provide new insights into the Solana Rug Pull landscape and support the development of effective on-chain defense mechanisms.
Financial systems have a growing reliance on computer-based and distributed systems, making FinTech systems vulnerable to advanced and quickly emerging cyber-criminal threats. Traditional security systems and fixed machine learning systems cannot identify more intricate fraud schemes whilst also addressing real-time performance and trust demands. This paper presented an Adaptive Neuro-Fuzzy Blockchain-AI Framework (ANFB-AI) to achieve security in FinTech transactions by detecting threats using intelligent and decentralized algorithms. The framework combines both an immutable, transparent and tamper resistant layer of a permissioned blockchain to maintain the immutability, transparency and resistance to tampering of transactions, and an adaptive neuro-fuzzy learning model to learn the presence of uncertainty and behavioural drift in fraud activities. An explicit mathematical model is created to explain the transaction integrity, adaptive threat classification, and unified risk based decision-making. The proposed framework uses Proof-of-Authority consensus to overcome low-latency validation of transactions and scalable real-time financial services. Massive simulations are performed in normal, moderate, and high-fraud conditions with the use of realistic financial and cryptocurrency transactions. The experimental evidence proves that ANFB-AI is always more accurate and precise than recent state-of-the-art algorithms and costs much less in terms of transaction confirmation time, propagation delay of blocks and end-to end latency. ANFB-AI performance supports the appropriateness of adaptive neuro-fuzzy intelligence to blockchain-based FinTech security.
The article examines the nature and multidimensional classification of digital financial assets (DFAs) as an emerging element of the modern financial system. It demonstrates that the rapid expansion of tokenisation has created a new class of instruments that combine the legal features of conventional financial rights with the technological advantages of distributed ledgers. Internationally, DFAs represent tokenised claims on cash flows, equity, debt, or other assets recorded in distributed or hybrid registers. In Russia, DFAs operate within permissioned information systems (OIS) and are mainly used for short-term debt issuance serving corporate and banking funding needs. The purpose of the research is to provide a holistic understanding of DFAs and to propose a multi-axis classification based on their economic function, underlying asset, holder’s rights, and circulation regime. The study employs analytical and comparative-legal methods, referencing international standards (MiCA, FATF) and industry datasets (DeFiLlama, RWA.xyz) together with official statistics of the Bank of Russia (ORFR). The findings refine the economic and legal definition of DFAs and highlight global and national market trends. It is concluded that the proposed classification enhances data comparability and provides a methodological framework for risk and performance analysis of DFAs. The results may serve as a foundation for the development of regulatory calibration and for aligning Russian market practices with international approaches.
Document authentication remains a pressing challenge in various domains, including financial services, academic credentialing, healthcare, and supply chain management. Existing centralized verification systems are vulnerable to manipulation, inefficiency, and limited transparency. Blockchain technology, with its immutability and tamper-resistant capabilities, offers a strong decentralized alternative; however, many current implementations lack structured, issuer-bound relationships for documents. This paper proposes a blockchain-based model that leverages a hierarchical token structure to authenticate and trace the provenance of high-value digital documents, with a focus on financial records. The model introduces the concept of an issuer-bound parent token and document-linked child tokens, enforcing a structured trust relationship between a legitimate institution and the documents it issues. By combining on-chain cryptographic hashing with off-chain file references, the approach is designed to balance verifiability with scalability. We implement a proof-of-concept using Ethereum-compatible smart contracts on a permissioned blockchain and evaluate it in a consortium-style financial setting. Our functional analyses demonstrate the model’s ability to ensure document integrity, provenance, and resistance to document fraud. This work offers a practical and extensible foundation for secure digital document authentication and verification in financial and other trust-sensitive settings.
Open access
Blockchain Technology Applications and Security
Cryptography and Data Security
Advanced Steganography and Watermarking Techniques
We introduce the Theory of Epistemic Abductive Geometry (TEAG), a framework for non-Bayesian inference grounded in admissible-support contraction under possibility theory. The central object is the TEAG quintuple \( \mathcal{E} = (H, \pi, \{H_\alpha\}_{\alpha\in(0,1]}, C, A) \), where evidence acts by contracting the geometry of admissible hypotheses rather than redistributing probabilistic belief mass. The falsification boundary is a tropical variety — exactly. Under the log-admissibility transformation \( \Phi(h) = -\log\pi(h) \), the canonical TEAG conjunctive update becomes tropical addition in the max-plus semiring: \( \Phi^+(h) = \Phi^-(h) \oplus \psi(h) = \max\!\bigl(\Phi^-(h),\,\psi(h)\bigr), \) where \( \psi(h) = -\log\kappa(y\mid h) \) is the surprisal of hypothesis h under observation y. The falsification boundary is the tropical variety of this polynomial: \( \mathcal{F} = \bigl\{h \in H : \Phi^-(h) = \psi(h)\bigr\}. \) This is the exact locus dividing surviving from falsified hypotheses: h is falsified if and only if \( \psi(h) > \Phi^-(h) \); it survives if and only if \( \Phi^-(h) \geq \psi(h) \). Within the class of possibility-theoretic recursive inference systems, this is, to the best of our knowledge, the first exact algebraic expression of Popper's falsification criterion: the boundary is the zero set of a tropical polynomial, determined entirely by the geometry of the prior impossibility and current surprisal fields. Main results. 1. Epistemic Contraction Theorem. Contraction is tropical addition: \( \Phi^+ = \Phi^- \oplus \psi \). Posterior α-cuts satisfy \( H_\alpha^+ = H_\alpha^- \cap E_\alpha(y) \): geometric intersection, not belief redistribution. The falsification boundary is the tropical variety \( \mathcal{F} \). 2. Possibilistic Cramér–Rao Bound (PCRB} For any filter in the class \( \mathcal{F} \) of epistemically admissible, contraction-based recursive estimators satisfying Axioms 2.1–2.5: \( \mathcal{E}_{\pi,k|k} \geq \mathcal{E}_{\pi,k|k-1} + \tfrac{n}{2}\log(1-I_k) \), where \( I_k \) is the Choquet integral of per-hypothesis surprisal against the prior possibility capacity. Within this class, the ESPF [28] is the unique filter achieving this bound with equality, and is therefore the unique minimax-entropy-optimal set-based recursive estimator under bounded epistemic uncertainty. 3. Tropical Hamilton–Jacobi structure (summary). The TEAG update is structurally consistent with a tropical Lagrangian \( L = T - V \), Legendre transform to a tropical Hamiltonian equal to the surprisal field, and a Hamilton–Jacobi equation whose solution is the tropical addition rule. The Euler–Lagrange equations on the epistemic manifold yield geodesic motion with explicit Levi–Civita connection and Christoffel symbols. This structure is interpretive and consistent with the axioms; full derivations are in the companion paper [31]. Taken together, this structure admits a precise interpretation: the TEAG update rule is a max-plus dynamical system whose governing equations have the same algebraic form as the Hamilton–Jacobi equations of classical mechanics, instantiated on hypothesis space rather than physical space. 4. Gaussian collapse. Probability theory is the collapse limit of TEAG as epistemic width \( W \to 0 \): Choquet converges to Lebesgue, the ESPF recovers the Kalman filter, and \( \mathcal{E}_\pi \to \tfrac{1}{2}\log\det\Sigma + \mathrm{const}(n) \). Probability is earned by evidence, not assumed. Epistemic neutrality and knowledge-system synthesis. Because TEAG's axioms require only a hypothesis space, a possibility field, and a contraction operator — not a probability measure, a likelihood function, or a frequentist grounding — heterogeneous knowledge systems can each instantiate the TEAG quintuple independently. Their joint admissible support intersection is the locus of coherence: the set of hypotheses neither system has falsified. No transformation of one system into the other's representational primitives is required. The composition theory (Section 6) formalizes the coupling architecture. Four instantiations provide the unifying structure: the ESPF [28] for recursive state estimation; the Geometry of Knowing [29] for measure-theoretic collapse; the minimax-entropy optimality proof [30]; and the Possibilistic Language Model (PLM, forthcoming [32]).
The agriculture sector plays a pivotal role in global economies, and optimizing its perishable food supply chain (PFSC) is vital to ensuring food security and transparency. The purpose of the study is to develop a blockchain-based smart contract to secure and provide transparency about perishable goods in the PFSC while delivering the goods between the stakeholders, such as farmers, mandis, and wholesalers. The study enhances collaboration between stakeholders by implementing smart contracts. The delivery status and the transactions have been safely recorded and verified by the stakeholder in the PFSC to ensure data integrity all the way through. The blockchain application has reduced fraud and streamlined the flow of goods and information. Moreover, this study emphasizes providing farmers with a straightforward route to the market to empower them. The benefits for the stakeholders are optimizing inventory control and developing appropriate decision-making skills. A three-echelon PFSC can become more resilient and is able to meet changing market demands by implementing blockchain-based smart contracts. Finally, the study employs blockchain technology to establish a decentralized and efficient PFSC, confirming a tamper-resistant system and enhancing stakeholder trust and collaboration.
This study examines the theoretical contributions of Masayasu Narumi, who served as a policy advisor to Mayor Ichio Asukata of Yokohama. He formulated influential ideas on local autonomy and local public finance. Despite his central role in shaping the theory of reformist local governments,Narumi’s theoretical contributions have not been sufficiently clarified. His work is characterized by its practical orientation, grounded in municipal reform and citizen participation, as well as its historical consciousness in addressing challenges and prospects for local governance. In the context of the 1980s “era of local autonomy,” when the essence of progressive local governments was under critical scrutiny, Narumi advanced a new paradigm such as a “policy-making government” or “citizens’ government.” In his theory of local public finance, Narumi conceptualized local autonomy and local public finance as integrated activities. He argued that the realization of reform and policy within a“ citizens’ government” required a decentralized administrative and fiscal structure between the central and local governments. His formulation of a“ decentralization-oriented fiscal theory” anticipated later debates on local choice and fiscal responsibility. As contemporary Japan again confronts the necessity of municipal reform, Narumi’s pioneering insights into participation, decentralization, and autonomy warrant renewed scholarly attention.
Blockchain technology has been widely used in the field of Internet of Things, providing effective support for solving security challenges in Internet of Things systems. However, due to the immature development language and deployment platform, smart contracts are prone to various vulnerabilities. Considering the immutability of smart contracts, efficient vulnerability detection before deployment is particularly critical. The existing detection methods have two main limitations: they can only identify a limited number of specific vulnerabilities, resulting in low coverage; the implicit correlation information between vulnerability types is ignored. In order to solve these problems, this paper proposes a smart contract multi-vulnerability detection model CorrelaScan (correlation-aware smart contract analyzer) that integrates vulnerability type correlation awareness. The model is based on a multi-task learning architecture, including a shared layer and a specific task layer. The shared layer uses BERT to extract shared features, while the specific task layer uses BiGRU to learn specific task features for vulnerability detection and type classification. In addition, a vulnerability type embedding module is integrated in the task-specific layer. The module mines potential associations by calculating the similarity between smart contract opcodes and vulnerability types, thereby enhancing detection guidance and improving model performance. Experimental verification on public datasets shows that the model can simultaneously detect 10 types of vulnerabilities such as integer overflow or underflow, reentrancy and timestamp dependence, with an average F1 value of 85.22%. Its detection performance exceeds the current state-of-the-art methods.
The landscape of e-commerce has witnessed a transformative shift in consumer behavior, driven by the rise of digital technologies and online platforms. As online purchases increase at an alarming rate, fraudulent activity has become a major concern for retailers and consumers alike. The objective of this research is to investigate methods for detecting fraudulent online transactions using machine learning algorithms. This paper proposes a Hybrid Agentic AI Architecture (HSAA) for edge-enabled e-commerce that incorporates intelligent agents and cryptographic security to enable real-time, trustworthy transaction processing. The architecture uses world-model distillation to enable efficient inference on edge devices. HSAA was tested on several large data sets such as a balanced credit card fraud set containing 2,952 transactions. The system scored 96.6% in detecting fraud, indicating very low false positives and high specificity. Negotiation exercises on 400 independent interactions were successful in 59%, with an average discount of 14.2%, using 1,142 zero-knowledge proofs that were verified with 100% validity. Some of the operational performance highlights include a throughput of 585 transactions per second, an average latency of 1.56 milliseconds, and a 81.9% reduction in bandwidth through selective state transfer. The findings support the argument that HSAA is a strong, secure, and high-performance edge-based e-commerce architecture, combining accuracy, efficiency, and reliability. Within HSAA, fraud detection functions as one of the core decision agents, while negotiation and secure execution mechanisms provide the broader operational context for trustworthy edge commerce. The architecture provides a solid basis for future studies in adaptive and autonomous AI-driven commercial systems.
Abstract Rural health systems are networks, which are geographically disseminated and resource limited, in which inefficient inter-hospital coordination has a strong influence on patient outcomes, operational stability and surgical resilience. Regardless of the development of smart hospital technologies, such as 5G-enabled communication opportunities, the integration of digital coordination centers, and telemedicine, the current frameworks are more focused on streamlining intra-hospital processes instead of the inter-hospital distribution of resources. This structural disintegration leads to slow shifts, poor use of bed space, inaccessibility of specialists, and poor responsiveness to surges. This paper suggests Smart Inter-Hospital Representation Network (SIHCN) to be a rural hospital ecosystem distributed systems architecture. The framework combines a granted blockchain based resource registry, real-time capacity monitoring strategies, specialist allocation registries, and adaptive routing logic into a coordination infrastructure. The proposed architecture will be able to guarantee decentralized system control against centralized command models, fault tolerance, and scalable interoperability among autonomous hospital nodes. The paper introduces a conceptual systems model that specifies the network topology, operational data flow, distributed resource synchronization and performance evaluation metrics. The simulation modeling is based on a scenario simulation that assesses the system performance when under routine and emergency surge conditions, showing that the transfer latency, resource balancing, and coordination efficiency is improved. The results make distributed ledger-based coordination a potential engineering technique in enhancing the resilience of rural health networks. This study also addresses the Healthcare Systems Engineering field by re-conceptualizing rural hospital coordination as a distributed resource optimization problem and suggesting an architecture-layer solution that can be applied to low-density, high-variability healthcare settings.
Smart contracts are autonomous systems that execute agreements using code. Their efficiency generated attention from a range of industries. The basis of traditional vulnerability detection techniques, opcode analysis, has limitations in detecting complex vulnerabilities. Our research aims to address these difficulties by developing an automated framework for vulnerability detection, mitigation, and patch deployment. Initially, smart contract data will be collected, followed by a preprocessing step to remove any unnecessary information using lexical analysis and Bidirectional Encoder Representations from Transformers (BERT). Then, the preprocessed data is used to identify the features that are relevant are selected. Following the features being selected, an intellectual engine is used to identify flaws. The intellectual engine that integrates the convolutional neural networks (CNN) and long short-term memory (LSTM) analyzes a subset of preprocessed data for vulnerabilities, with explainable artificial intelligence (XAI) evaluating the importance of each feature to predictions. Our method produces exceptional outcomes with a 99.25% precision, 99.76% accuracy, 99.60% F1-score, and 99.36% recall. Smart contract vulnerability identification, mitigation, and patch generation are improved by the proposed Beluga Crayfish Optimization Algorithm (BCOA) and Crayfish Secretary Bird Optimization Algorithm (CSBOA) together with graph neural networks (GNN). In addition to producing the required fixes, this method offers efficient mitigation techniques. Therefore, it greatly enhances smart contract security and efficiency. In the end, smart contract programs that use this integrated approach are more secure.
Abstract: Remote electronic voting systems require tamper-evident records of ballot submissions, yet the ledger integrity problem - ensuring that the record cannot be silently altered after the fact - has received less formal treatment than ballot-phase cryptography. Existing approaches rely on distributed blockchain consensus, digital signatures on bulletin boards, or external hash-tree timestamping services, each introducing dependencies on specialized infrastructure, continuously trusted parties, or computationally intensive proof systems. This paper provides a formal security analysis of a hash-chained append-only ledger instantiated in a standard relational database with quorum replication, establishing that equivalent tamper-evidence guarantees are achievable under the sole assumption of collision resistance of the instantiated hash function. We define five security properties in the Bellare-Rogaway game-based framework. Tamper-evidence (Proposition 1) bounds any PPT adversary's advantage at 2q(λ) · Adv^CR_H, with a tight reduction to collision resistance. Fork-resistance (Proposition 2) establishes that an adversary corrupting fewer than a quorum threshold of replicas cannot cause divergent chain histories at honest replicas. Retrospective modification resistance (Proposition 3) establishes that post-close modifications are detectable by any auditor holding a real-time replica copy. Cross-ledger binding security (Proposition 4) extends these results to the dual-ledger construction of the Arcaunt architecture, in which a public ballot ledger and a temporal credential ledger are cryptographically bound at insert time, making modifications to either detectable through the other with advantage bounded by 4q(λ) · Adv^CR_H. Selector integrity (Proposition 5) establishes that the last-valid-vote rule - operating on insertion sequence rather than timestamps, making it immune to clock manipulation - is integrity-secure conditional on credential security, formally delineating the boundary between ledger and credential security domains. We apply an eight-metric comparative framework to seven e-voting integrity architectures - hash-chaining, bulletin boards, homomorphic tallying, mixnet-based systems, blockchain, KSI timestamping, and VVPAT hybrids - establishing three findings: tamper-evidence basis is universal but mechanism-specific; fork-resistance is architecturally necessary specifically for revoting-based systems; and auditability complexity is inversely correlated with cryptographic sophistication. The hash-chained relational ledger achieves collision-resistance-based tamper-evidence with O(n) verification accessible to any SQL-capable auditor - a design point unoccupied by existing systems under the same combination of properties. Prototype validation on a Firebird 5.0 implementation confirms that each proposition is instantiated by a specific database trigger mechanism, with 6ms mean ballot submission latency and O(n) verification complexity empirically confirmed.
Bitcoin is one of the most widely used cryptocurrencies. It offers decentralization, transparency, and Pseudonymity. However, this leads to money laundering, illegal activities, and financial scams. Malicious users also try to bypass transparency by using third party mixing services to conceal origins of users and due to the vast number of transactions it becomes difficult to detect the ownership of bitcoin wallets. To address these challenges, profiling ownership of the bitcoin addresses becomes necessary. The proposed research compares traditional clustering techniques with transaction pattern analysis to identify which wallet addresses belong to which entities. Gini Impurity measure is used to evaluate how accurately the clusters are developed to detect ownership. Uncovering the relationship between these addresses is necessary to understand the behavior of users. This helps in identifying suspicious activities in the bitcoin network by mapping relations between the wallet addresses. This could aid in Anti-money laundering as a tool for crypto-forensics.
Nepal?s federal transition has shifted major responsibilities for basic and secondary education to local governments, including rural municipalities. This paper examines how they are using this mandate and what it implies for education equity. Using qualitative analysis of constitutional and legal texts, national sector plans, and municipal education policies and budgets, it focuses on three domains: governance capacity, fiscal capacity, and the alignment of policy and practice. Rural Municipalities (RMs) are beginning to institutionalize their role through education sections, local acts and annual plans, and, in some cases, substantial budget allocations to education. However, legal ambiguities, reliance on earmarked federal grants, limited administrative capacity and politicized teacher management restrict their room for maneuver. These constraints create a gap between rights-based commitments and everyday schooling, especially for disadvantaged children in remote areas, and highlight the need for clearer roles, stronger local capacity and more equitable, flexible financing and accountability.
.This article extends the theoretical framework of Rényi extropy by establishing new properties, including its convergence to information extropy under limiting parameter conditions and its ability to assume both positive and negative values. Furthermore, it explores the interrelationships among Rényi, information, and Tsallis extropies, providing a unified perspective on these uncertainty measures. To demonstrate its practical utility, we apply Rényi extropy to analyze uncertainty in cryptocurrency markets, specifically Bitcoin (BTC) and Ethereum (ETH). Our findings reveal its superior capability in capturing non Gaussian dynamics and assessing risk compared to traditional entropy-based methods. Furthermore, we integrate machine learning techniques, including Extreme Gradient Boosting (XGBoost) and k-Nearest Neighbors (kNN) to predict BTC and ETH prices, validating the synergy between advanced statistical measures and computational forecasting. The predictive performance is evaluated using advanced XGBoost and k-NN models, assessed through RMSE and R2 metrics. The results demonstrate a significant improvement over traditional benchmarks, including Shannon entropy and ARIMA, in forecasting risk-adjusted returns. The empirical results underscore Rényi extropy’s potential as a robust tool for financial market analysis and risk management.