The development of smart contracts on distributed ledger technology has created very real doctrinal and evidentiary problems for the classical consent theory-based legal system. This article conducts a thorough comparative study on the legal regimes of defects of consent error, fraud, duress and misrepresentation regarding smart contracts in the light of the international conventions adopted by the United Nations Commission on International Trade Law (UNCITRAL) and the United Nations Principles of International Commercial Contracts (UNCPC). The study highlights key gaps in legislation and clear issues of evidence that hinder claimants from establishing vitiated consent in algorithmically executed contracts, grounded in primary legislative sources, such as the UAE Federal Law No. 5 of 1985 (Civil Transactions Law), UAE Electronic Commerce Law No. 1 of 2006, the regulatory frameworks of the Dubai International Financial Centre (DIFC) and the Abu Dhabi Global Market (ADGM), and Jordanian Civil Code No. 43 of 1976. The analysis demonstrates that, while automated self-executing code involves one-to-one interaction between a digital entity and a human user, both jurisdictions are poorly suited to deal with these types of interactions, as the record cannot be altered and the party deploying the code can be anonymous, and the 'agreement' can be either ambiguous or impossible in practice. The article suggests a three-part reform agenda – (i) technology-neutral statutory amendments to explicitly apply the doctrine of “defect of consent” to algorithmic agents; (ii) forensic evidentiary rules for the authentication of blockchain data and expert testimony; and (iii) a specialised dispute resolution mechanism based on the ADGM's current smart contract recognition framework. The findings add to the still emerging literature on smart contract legality in the Arab world, and provide practical suggestions for legislative reform.
Ruziev Ulugbek Shukhrat Ugli, Seok-Yoon Kim, Youngmo Kim, Sun-Jib Kim
In OTT (Over-The-Top) service environments, reliable recording and verification of content usage history are becoming increasingly important for copyright protection, royalty settlement, and dispute resolution. However, conventional platform-specific log management methods have limitations in interoperability, integrity assurance, and external verifiability. In addition, simple indicators such as the number of views or total playback time are insufficient to accurately reflect actual OTT viewing behavior, which often includes nonlinear interactions such as pause, seek, replay, and early stop. This paper proposes a platform-independent event-based verification method for OTT content usage history. The proposed method defines usage history metadata consisting of content information, session information, playback event information, and usage result information, and derives valid usage intervals from playback events such as play, pause, seek, replay, and stop. In addition, hash-based verification information and linked recording structures are applied to the generated usage history to enhance integrity and traceability. To examine the applicability of the proposed method, processing latency in the usage history aggregation stage was measured according to changes in the number of sessions and platforms. The experimental results show that the processing time increased in a stable manner as the session scale increased, indicating that the proposed method can be applied to large-scale OTT usage environments. This study is meaningful in that it presents a platform-independent and verifiable method for organizing OTT content usage history, and it can be further extended through future performance evaluation of blockchain recording and verification processes in diverse service environments.
The metaverse is emerging as a persistent and immersive digital environment that combines extended reality, cloud edge computing, artificial intelligence, big data analytics, digital twins, blockchain, and future wireless connectivity. However, real time metaverse services require ultra low latency, high data rates, context aware intelligence, scalable cloud infrastructure, and trustworthy data governance. This paper presents a PRISMA informed systematic review of AI driven cloud and 6G enabled metaverse research with special attention to big data analytics, security, privacy, and digital trust. A structured search strategy was designed across major scholarly databases and citation snowballing sources, and 45 studies were selected for qualitative synthesis. The review classifies the literature into six themes: AI and real time analytics, cloud edge device orchestration, 6G connectivity, metaverse security, privacy preserving mechanisms, and digital trust governance. The findings show that 6G and edge intelligence can support immersive metaverse services through sub millisecond interaction, distributed rendering, semantic communication, integrated sensing, and adaptive resource allocation. At the same time, the literature reveals open challenges involving identity management, biometric privacy, adversarial AI, cross platform interoperability, data provenance, and user trust. The paper concludes with a conference oriented research agenda for trustworthy AI cloud 6G metaverse systems.
Blockchain platforms enable transparent and immutable solutions for verification of proofs of digital claims, permissions, states, and events. However, numerous decentralized applications still force their users to operate with wallets, native tokens, and transaction costs, even though they have nothing to do with their verification purpose. This issue makes decentralized systems less user-friendly and constrains their adoption among not technically proficient Web3 users. This paper explores different patterns for gasless verification of proofs on decentralized platforms. The research focuses on five architectural patterns of such decentralized proof systems, including read-only blockchain verification, off-chain signature verification, relayer-based meta-transactions, account abstraction with paymasters, and hybrid on-chain/off-chain proof anchoring. Each architecture is analyzed qualitatively considering its usability, cost-effectiveness, decentralization, security, scalability, and implementation complexity. The results show that gasless verification enhances the usability of such systems, but at the same time transfers responsibility of trust assumptions to relayers, paymasters, backend servers, signature protocols, and off-chain data availability mechanisms. In this regard, potential risks, which include replay attacks, centralization of relayers, malicious paymaster activities, uncertainty of signers’ identity, and dependence on backend servers, are discussed. Besides, the paper offers a decision-making framework for choosing one of gasless verification architectures depending on the presence/absence of state change, authority of proofs, required verification frequency, degree of decentralization needed, and level of technical maturity of system users.
Decentralized Autonomous Organizations (DAO) are an emerging blockchain-based paradigm for decentralized governance. Despite growing interest, their conceptualization remains fragmented. This paper introduces DAO-Ontology, a domain ontology formalizing DAO concepts-including perspectives, characteristics , solutions, evaluation methods, application domains, and challenges. Developed via the OntoView methodology from a systematic mapping of 47 studies, it is implemented in OWL and validated with a Java application using the OWL API. The ontology provides a standardized vocabulary, supports semantic integration, and enhances understanding of DAO as sociotechnical systems.
This paper explores the deployment of a blockchain supported land-registry system in rural Bihar. In light of transparency, fraud mitigation, governance efficiency and digital inclusion, this paper refutes the common assumption that immutability of data results in accurate title. This study employs validated secondary data, from the years 2020-2025, such as the Bihar National Family Health Survey 2019-2021, various official sources of the Digital India Land Records Modernization Programme, Bihar land-service portals, and peer-reviewed literature on the intersection of blockchain and land governance. According to the National Family Health Survey (NFHS)-5, approximately 84 percent of surveyed households in Bihar were classified as rural, and a majority of the respondents, 79.4 percent of women and 56.4 percent of men, had never used the Internet. The widening of the access gap was examined in the context of the mobile phone ownership and usage, the financial inclusion of women, as well as the self-reported ownership of a house or land. The evidence-weighted readiness assessment determined that the level of digitization was relatively better, but the level of coordination of institutions, governance of cybersecurity, design of correction mechanisms, and design of user participation mechanisms were relatively poor. This paper proposes a permissioned industry consortium ledger, where sensitive data and documents remain off-chain, and the only data recorded on-chain are the hashes, identifiers, approvals, timestamps and version references of the land parcels. Smart contracts are used to manage the workflows from registration to mutation, but are not used to resolve the issues of contested titles, inheritance, or boundaries. This paper proposes an assisted-access model with a phased implementation approach, a multilingual interface, an appeal mechanism, and gender-disaggregated analysis and evaluation. Rather than fabricating field surveys and administrative performance data, this paper presents a complete primary data collection framework with a detailed statistical analysis plan for empirical assessment.
This dissertation explores the impacts of blockchain-based audit trails and stakeholder feedback loops on ethical conduct in U.S. federal and state public administration. The study addresses ongoing ethical problems in government, such as low levels of accountability, inadequate procedures, policy manipulation, and a waning public confidence in government, despite the existence of government ethics codes and oversight bodies. Ethical adherence is considered using two complementary theories: the virtue theory (Honesty, Fairness, Integrity, Responsiveness, Trust) and the deontological theory (Duty fulfillment, Rule following, Documentation, Accountability, Procedural compliance). The study was conducted with closed and open-ended answers from public administration participants who have experience in ethics, compliance, audit, procurement, oversight, stakeholder engagement, or other related areas using an anonymous online survey via Google Forms. Ten responses were received and nine valid responses were analyzed. The findings revealed that audit trails linked to blockchain were seen as the most beneficial for holding accountable, tracing, ensuring record integrity, reviewing and ensuring duty-based compliance. Stakeholder feedback loops were seen as most beneficial for fairness, honesty, telling the truth, responsiveness and public trust. The two tools were perceived as complementary tools and not as competing tools. The results, however, also showed that transparency instruments are not necessarily the drivers of ethical behavior. They rely on leadership, staff development, correct data entry, active data review, meaningful follow-up to feedback, and follow-through on actions. The study brings to the public administration literature a connection between mechanisms of transparency and ethical adherence in both virtue-based and duty-based approaches. It provides pragmatic advice to agencies that want to build ethical governance through technology, participation, and organizational culture in complex federal, state, and government-adjacent administrative contexts, in everyday practice.
A multi-agent transaction optimization decision-making method based on blockchain Berge-NS equilibrium is proposed, aiming to protect user interests and achieve carbon reduction objectives. Firstly, by improving the utility function of electricity users and quantifying the impact of blockchain technology on the electricity utility of market entities, a blockchain based P2P electricity trading architecture for microgrids is constructed, and a blockchain network and utility function for electricity users are designed; Secondly, the Evolutionary Game Theory based on bounded rationality decision-making is introduced to construct a Berge-NS game model on both sides of electricity supply and demand. The distributed iterative algorithm and step size control method are used to solve the Nash equilibrium, and the strategy evolution of demand side subjects in the game process is studied through dynamic processes; Finally, numerical simulations were conducted to analyze the trading strategies of bilateral contract markets, centralized trading markets, and dual layer decision-making models for electricity sellers, verifying the feasibility and effectiveness of the models and algorithms. The experimental results show that the proposed multi-agent trading Berge-NS decision-making method for microgrid electricity market exhibits certain performance advantages in reducing carbon emissions, lowering user electricity costs, and improving user satisfaction.
This research presents a novel decentralized identity management system leveraging Ethereum smart contracts and cryptographic protocols to enable self-sovereign digital identities. Traditional centralized identity solutions pose risks related to data privacy, security, and user autonomy. To address these limitations, we propose a blockchain-based architecture integrating smart contracts for identity governance, IPFS for decentralized attribute storage, and threshold cryptography for private data recovery. Our dual-contract model (Identity Contract and Recovery Contract) facilitates secure identity creation, verifiable attribute attestation, and robust key recovery through social consensus. We introduce a privacy-preserving mechanism using encryption-key splitting among trusted peers to enable recovery of encrypted off-chain data. Implementation uses Web3.js, Solidity, and QR-code-based communication, abstracting cryptographic complexities from end-users. Security analysis addresses replay, man-in-the-middle, Sybil, and multi-user compromise attacks with mitigations including challenge-response authentication and time-delayed contract execution. This work contributes a cryptographically secure, user-centric identity framework ensuring data sovereignty, recoverability, and interoperability within the Ethereum ecosystem.
Across Bitcoin and the blockchain-art discourse, four recurring claims can be distinguished: trust minimization, authenticity, scarcity, and preservation. A deliberately critical corpus documents each as displaced rather than delivered: trust is relocated, authenticity is located in a registry, scarcity is manufactured, and preservation is asserted rather than achieved. This article reads these displacements against an analogue rule system running since 1 January 1993, in which each calendar day constitutes one work whose Authentic Number and price are fixed by formula, and which remains in suspension until a collector realizes it as a print. The comparison is structural: not protocols versus persons, but a centrally authored, personally warranted rule against a permissionless state maintained through distributed consensus. The formula makes each day's identity and price independently computable; a public realization record publishes which days have been realized; and two named authors — Christopher Temt (concept and text) and Eugen Kment (drawing) — sign the physical work and answer for the remaining claim. Accountable means exactly this: the non-computable claims have identifiable bearers and are publicly contestable. The system does not replace trust with proof. It separates what can be computed from what must be answered for in person.
The paper presents a new hybrid approach (LSTM-RF-SLSQP) that acts as an advanced decision-support tool for institutional crypto portfolio management. The methodology includes the use of LSTM neural networks to detect non-linear temporal patterns and RF algorithms to detect structural market noise. The ability to predict future prices based on LSTM-RF model has been extensively verified out-of-sample using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), and shown to have smaller prediction error than standalone algorithms on very volatile assets. Then, based on robust return and risk forecasts, SLSQP optimization algorithm allocates asset weights aiming at maximizing Sharpe ratio under specific institutional constraints, applying buy-and-hold approach with quarterly rebalance. The empirical study is performed for a portfolio of ten major cryptocurrencies (BTC, ETH, SOL, BNB, XRP, ADA, DOGE, TRX, LINK, DOT) using data provided by KuCoin exchange from January 2024 to January 2026. The numerical experiments demonstrated the significant superiority of the suggested framework compared to all benchmarks. Namely, the LSTM-RF-SLSQP approach provides the impressive annual return of 107.40%, Sharpe ratio of 2.04, with maximum drawdown equal to -3.80%.
Fernando Henrique Antunes de Araujo, Milena Kojić, Petar Mitić, Kerolly Kedma Felix do Nascimento · 5 authors
This study applies a prespecified dynamic MFDFA workflow across pandemic, geopolitical-conflict, and tariff-policy regimes for ten non-stable, long-history cryptoassets selected ex post from the 23 July 2026 market-cap ranking. The common sample comprises 3155 daily log returns per asset from 2 December 2017 to 22 July 2026; the final endpoint regime includes the U.S.–Iran conflict. The estimator uses q=−10,−8,…,10, linear detrending, 22 scales from 16 to N10, adjacent-secant Legendre transformation, a cubic spectrum peak, and 500-day windows stepped by 21 days plus a terminal endpoint. Full-sample IE=|α0−0.5| ranges from 0.01397 (LINK) to 0.08706 (BNB), and observed width ranges from 0.32625 to 0.74678. The controlled incremental U.S.–Iran endpoint coefficient is −0.02734 (two-way clustered SE 0.02369; p=0.2489), with asset-cluster t(9) interval [−0.08226, 0.02759]. Quantile estimates range from +0.00206 at the 0.10 quantile to −0.04769 at the 0.90 quantile; all five 999-replication asset-cluster bootstrap percentile intervals include zero. Exact rolling sensitivities are negative for the 500/14, 500/30, and 730/30 designs but positive for the 250/21 design, and every small-cluster interval includes zero. Direct spectrum-width contrasts also remain nonsignificant after Holm adjustment. None of ten observed widths survives BH correction in 200 shuffled-return surrogates per asset (2000 fits in total). The results document heterogeneous and specification-sensitive dynamic multifractal patterns, not isolated or causal crisis effects.
A fixed-odds contract on short-horizon price direction has a positive expected value only when its win probability exceeds the break-even rate implied by the payout ratio. A deployable predictor must also produce signals at a sufficiently stable rate. We formulate this setting as selective prediction with a coverage constraint and combine a five-seed gradient-boosting ensemble over a 90-dimensional causal feature panel with daily adaptive quantile thresholds, each estimated from the preceding 14 to 28 days of model scores, with parameters selected on training data alone. Configurations are frozen after three chronological pseudo-out-of-sample folds and evaluated on a held-out period from 1 January to 10 June 2026, and the whole procedure is then repeated on a quarterly re-freezing cadence over seven successive windows. Across BTC and ETH at 5- and 10-min horizons, with a payout of 0.8 and a 55.56% break-even rate, the models execute 10.4 to 11.0 trades per day, and all four selective win rates exceed break-even. Under a dependence-aware block bootstrap, three of four remain significant, and within a 32-test confirmatory family, two survive Holm–Bonferroni correction. Coverage stays inside the operational band in 26 of 28 re-frozen windows. Compared under one execution protocol, a fixed calibration slice drifts out of band while a trailing window does not, and adaptive conformal inference (ACI) matches the proposed rule on coverage when its step size is tuned but not otherwise, whereas an outcome-driven conformal controller reduces coverage by more than an order of magnitude. The expected value is insensitive to exchange fees, which consume under 5% of the measured edge, and sensitive to the payout term. Under matched feature sets, training pools, and coverage, most of the apparent cross-asset difference does not persist. This paper presents a proof of concept for the framework rather than making any claim about cryptocurrency predictability.
This study examines whether major cryptocurrency returns respond systematically to scheduled Federal Reserve (Fed) interest rate announcements and whether FOMC-window movements are explained more by realised policy decisions or by broader risk-sentiment conditions. Using daily data for Bitcoin, Ethereum, XRP, Dogecoin, Solana, the U.S. Dollar Index, and VIX, the analysis covers 43 scheduled FOMC announcements between 2021 and 2026. Six cumulative event-window returns are evaluated through parametric mean tests, Wilcoxon signed-rank tests, and panel event-study regressions with crypto fixed effects and FOMC-event-clustered standard errors. Because the available surprise measure contains only two nonzero observations, the study focuses on realised rate changes, hike/cut/hold categories, asymmetric rate-change magnitudes, VIX changes, and DXY returns rather than formal monetary policy shocks. The results provide little evidence that cryptocurrency returns differ systematically from zero around FOMC announcements. Actual rate changes, policy-direction categories, and asymmetric hike/cut magnitudes do not robustly explain event-window returns, and crypto-specific interaction models provide no stable evidence of heterogeneous sensitivity across assets. By contrast, VIX changes are negatively and significantly associated with cryptocurrency returns in several windows, while DXY effects are weak and unstable. The study contributes by showing that FOMC-window cryptocurrency performance is better explained by risk-sentiment conditions than by the realised size or direction of Fed rate decisions.
Pham Ngoc Toan, Le Tran Trung Hieu, Nguyen Vu Trung Nguyen
Carbon pricing is jurisdictional, while proof-of-work cryptocurrency mining is a highly mobile electricity load. We examine whether daily power-sector emissions display a cross-regional and distributional pattern consistent with short-run emissions displacement. Using daily observations covering calendar years 2019–2025 (with a boundary observation on 1 January 2026; N = 2550 after transformation and cleaning), we estimate quantile regressions for the EU27, the Russian Federation and the rest of the world using the interaction between Bitcoin returns and European carbon-allowance returns. The focal Russian lower-tail interaction is positive (q10 beta = 0.0662); OLS and dynamic specifications remain positive, and a 1000-replication pairs bootstrap gives p = 0.0077. The association survives a trading-day-only sample, calendar and persistence controls, and a seven-lag specification, while randomised-carbon and non-power-sector placebo outcomes are null. However, the coefficient loses conventional significance without Winsorisation, the May-2021 Chinese-ban timing prediction is not supported, and a direct EU27-minus-Russia substitution diagnostic is null. Quantile-on-quantile estimates place the largest Russian Bitcoin-return coefficients in high-carbon-price, low-emission states, but remain descriptive. Because the design does not observe mining capacity moving across jurisdictions and the available full-sample Russian emissions series is national rather than subnational, the evidence supports a leakage-consistent operational association rather than proof of physical relocation or a broad causal effect of EU carbon pricing.
Blockchain systems provide a decentralized and fault-tolerant infrastructure for maintaining a shared transaction ledger without relying on a trusted central authority. Nakamoto consensus, in particular, enables open participation and robust agreement in permissionless environments. However, these benefits typically rely on broad data replication, which requires participants to download and propagate large volumes of transaction data and can therefore impose substantial communication overhead. This thesis proposes and analyzes a bandwidth-efficient data recoverability protocol for Nakamoto consensus using erasure-coded sampling. Instead of requiring every participant to download a full transaction batch, the protocol allows an operator to encode a large transaction batch, called a mega transaction, into coded chunks and publish a compact cryptographic commitment on chain. Participants verify sampled coded chunks and cast PoW-bound votes on their validity. The Nakamoto consensus layer then determines whether the mega transaction should be accepted as recoverable, so that it can be reconstructed and verified later if a dispute arises. The main focus of this thesis is to formalize the recoverability failure event: the event that the protocol incorrectly accepts a mega transaction as recoverable even though honest participants do not collectively hold enough valid coded chunks for reconstruction. We derive conservative analytical bounds on the probability of this event and use these bounds to formulate a utility-based parameter-selection problem under a target security requirement. Monte Carlo estimates validate the analytical bounds, and numerical results illustrate the tradeoff between recovery communication overhead and confirmation latency.
Commitment branching is a novel approach to modeling strategic interaction in multi-agent systems, particularly within the context of blockchain and decentralized autonomous organizations (DAOs). This paper introduces the concept of a state [s] that can potentially support multiple commitments, denoted as [P] and [Q]. These commitments lead to distinct computational trajectories, represented as [P → T_P] and [Q → T_Q]. The core of the model lies in the definition of B_C(s), which quantifies the number of distinct branching possibilities originating from a given intermediate state. This branching behavior directly reflects the potential for divergent strategies and the inherent complexity of decentralized decision-making. The model offers a simplified yet powerful framework for analyzing the dynamics of commitment and its impact on system evolution. Further exploration of this framework could lead to improved strategies for managing risk, optimizing resource allocation, and enhancing the robustness of decentralized systems.
Food supply chains continue to be susceptible to fraud, contamination incidents, and unclear provenance records, which erode consumer confidence and significantly harm the world economy each year. Because blockchain technology provides immutable, shareable, cryptographically verified ledgers among people who distrust each other, it is frequently suggested as a solution. The oracle problem, however, is inherited by the majority of deployed systems: a ledger ensures that recorded data is not altered, but it does not ensure that the data was accurate when it was entered. In addition to reviewing the opportunities it presents for food safety and sustainability reporting, this study examines the technological, financial, and regulatory obstacles of blockchain-based food traceability and proposes a new architecture called the Dynamic Trust-Weighted Oracle Consensus (DTW-OC) framework. We present the architecture, the scoring algorithm, a comparison against Proof-of-Work, Proof-of-Stake, and PBFT, an example dairy cold-chain scenario, and a research agenda for standardisation and interoperability. DTW-OC introduces a reputation-weighted, cross-validated oracle layer that scores every IoT sensor and human data source in real time and feeds that score into block-validator selection, so a source's influence on the ledger is proportionate to its demonstrated reliability.
PQ-Sortition is a post-quantum cryptographic sortition protocol constructed from the NTRU lattice hardness assumption and instantiated using Falcon-512 (FN-DSA). The construction uses deterministic Falcon signing to obtain a reproducible, publicly verifiable proof and combines it with a consensus-layer commit-then-reveal mechanism to address the lack of unconditional uniqueness inherent in GPV-style lattice signatures. The work introduces NTRU-Sortition, a many-time lattice-based verifiable random function construction, and provides formal analyses of third-party uniqueness, pseudorandomness under the NTRU-SIS assumption in the Random Oracle Model, and provability. The paper further defines PQ-Sortition as a post-quantum proof-of-stake leader-election protocol using a historical randomness beacon, stake-weighted sortition, adaptive difficulty, equivocation slashing, and grinding resistance. The Falcon-512 instantiation provides a 32-byte output and proofs of up to 666 bytes. The paper also presents concrete performance measurements, security parameters, consensus integration details, comparisons with prior post-quantum VRF constructions, and open research problems.
Secure financial transactions require more than just an immutable record — they also demand privacy-preserving identity assurance (which enables secure, trusted and transparent communication), adaptive fraud intelligence (to detect fraudulent transactions), policy-aware execution (so organizations can set their own rules for data use), resilient consensus (enables multiple parties to agree on data use), and auditable records within a single low-latency pipeline. Current permissioned-blockchain solutions often have independent optimizations for authentication, access control, fraud detection, consensus and auditing; as such, these separate areas lead to fragmented security decision making, unnecessary disclosure, static endorsement policies and throughput–latency tradeoffs. The research presented here describes FinTrust-X, a cross-layer risk-adaptive permissioned blockchain architecture where the security state created by each layer is used to create the next. A Zero-Knowledge Context Adaptive Role and Trust Authentication System (ZK-CARTA) provides zero knowledge context adaptive role and trust authentication to enable verifiable credentials to be selectively disclosed based on user device/session context and dynamically authorize users to minimize identity exposure and privilege abuse. Users are provided authenticated evidence to feed a Temporal Graph Transformer (TRiG-FraudFormer) that models joint transactional, account, device, merchant, beneficiary and trust relationships to produce a calibrated fraud-risk assessment along with counter-factual explanations. Risk is converted into adaptive smart contract paths, confidence levels and endorsement requirements to minimize unnecessary verification overheads. Safety constrained reinforcement learning is applied in RA-BFTune to adaptively optimize batching, ordering and Byzantine fault tolerant consensus based on transaction risk and network-states. Continuous cryptographic audit evidence is produced in PQ-AuditTwin utilizing immutable provenance, Merkle verification and ML-DSA-based post-quantum signature generations. Feedback regarding changes/drift in previous layer inputs is returned to those layers. Targeted validation results show ROC-AUC values of .96-.98 and F1 values of .92-.95 were achieved in addition to achieving authentication times less than 30ms., 1500-2000 TPS, P95 response time < 700ms, and greater than a 90% reduction in unnecessary disclosure of sensitive data from users indicating significant improvements in confidentiality, fraud-resilience, authorization-efficiency, scalability and auditability when compared against multi-organization Fabric workloads that included injected fraud and Byzantine faults.
Today, biometric authentication has become a central component of user security in social governance systems, where each government department demands access to user-specific data that varies across agencies. However, storing such data in centralized repositories increases serious privacy concerns, as unrestricted access by multiple entities maximizes the risk of data leakage. To address this, our research presents a novel biometric authentication system integrating robust privacy-preserving techniques, built on advanced deep learning architectures and differential privacy algorithms. A blockchain ledger integrated with a Merkle tree is used to securely store user identities, providing tamper-evident cryptographic validation of registered users. We further develop a novel hybrid model by integrating a pre-trained Vision Transformer (ViT) with a differential privacy-based machine learning enhanced training strategy, wherein the model is trained on noise-induced images to resist inference attacks. The system without differential privacy achieves 90.80% accuracy, 0.94 precision, 0.91 recall, and an F1-score of 0.90 in the standard configuration, while the differentially private model maintains 68.97% accuracy with ε = 6.2, ensuring a strong privacy—accuracy balance. The evaluation confirms that our proposed model, incorporating differential privacy, provides a secure and scalable solution for managing sensitive citizen data while achieving reliable performance in privacy-aware biometric verification for real-world e-governance applications.
Indonesia's national engineering accreditation system faces systemic inefficiencies, governance limitations, and data integrity challenges that hinder international recognition under the Washington Accord and impede the global mobility of engineering graduates. These shortcomings are compounded by fragmented audit trails, opaque decision-making processes, and vulnerability to credential fraud, which undermine trust in accreditation outcomes. This study aims to design a model-based system engineering framework for a blockchain-enabled accreditation system that enhances data integrity, transparency, and traceability to support evidence-based decision-making and Washington Accord compliance. A Systematic Literature Review (SLR) guided by the PRISMA protocol was conducted, synthesizing insights from 109 selected studies on blockchain implementation in accreditation and credential verification systems. The study adopts a Model-Based System Engineering (MBSE) approach using the Requirement-Functional-Logical-Physical framework to translate requirements into a structured system architecture. The proposed framework integrates Hyperledger Fabric as a permissioned blockchain network for immutable record-keeping and the InterPlanetary File System for decentralized off-chain document storage, creating a hybrid on-chain/off-chain architecture. The resulting five-layer framework comprises Participants, Digitalized Access Points, Communication, Distributed Ledgers, and Existing IT Systems layers. Key findings demonstrate that the proposed architecture significantly enhances data integrity through cryptographic verification, provides transparent and tamper-proof audit trails for all accreditation activities, enables real-time verification of document authenticity, and supports interoperability among diverse stakeholders. The framework strengthens decision-making by providing verifiable evidence for informed judgments, ensuring accountability through transparent record-keeping, and enabling cross-border trust in accreditation decisions. This research concludes that the blockchain-IPFS integrated framework offers a scalable and trustworthy pathway for transforming Indonesia's engineering accreditation system, addressing both domestic governance challenges and international compliance requirements under the Washington Accord.
This article develops a finance-oriented conceptual assessment of Sui, an object-centric Layer-1 blockchain. The analysis draws on peer-reviewed research on scalability, smart contract execution, tokenomics, decentralized finance risk, market microstructure, sustainability, and regulation. It also uses a limited set of Sui-specific academic and official technical sources to interpret protocol design. The review focuses on three features: an object-centric state model that can support parallel execution when transaction states remain sufficiently partitioned; the Move language, which uses resource-oriented semantics to constrain selected asset-handling risks; and a directed acyclic graph-based consensus pipeline intended to reduce unnecessary coordination for suitable workloads. These features are linked to finance-relevant outcomes, including execution reliability, liquidity formation, adoption persistence, market resilience, and institutional investability. The assessment remains conditional. Shared-object contention may weaken realized performance, composability may preserve important classes of smart contract risk, and token emissions may dilute the value created by ecosystem growth. Regulatory uncertainty and sustainability scrutiny also influence the institutional perimeter of the asset. The article contributes an evaluation matrix, a conceptual framework, and a set of propositions for future empirical testing. No causal or statistical inference is claimed. The central conclusion is that Sui's architecture is economically relevant only when technical performance, assurance capacity, tokenomics discipline, and institutional conditions develop together. Received: 14 April 2026 | Revised: 8 July 2026 | Accepted: 27 July 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Low Jun Yan: Conceptualization, Methodology, Formal analysis, Investigation, Writing – original draft. Md Sharif Hassan: Methodology, Validation, Writing – review & editing, Supervision, Project administration. Nguyen Mai: Resources, Writing – original draft, Visualization.
T G Keshavamurthy, S. Guruprasad, K. N. Hareesh, M. V. Chidananda Murthy · 6 authors
In recent years, smart IoT systems pose significant challenges regarding security, scalability, and intelligent decision-making for IoT data, particularly amid advances in quantum computing attacks. Traditional cryptographic and machine learning techniques seem insufficient for real-time IoT systems where data is dynamic and large-scale and security requirements are strict. In this paper, a novel Post-Quantum Probabilistic Hidden-State Deep Learning (PQP_HS_DL) framework is presented that comprises of effective probabilistic hidden state modelling, lattice-based post-quantum cryptography and blockchain technology to process IoT data securely and efficiently. In the proposed PQP_HS_DL framework, a probabilistic hidden state model is applied to learn temporal dynamics and uncertainty in IoT data streams to provide enhanced prediction and reliable anomaly detection capabilities. A lattice-based cryptographic scheme ensures quantum-resistant security, and blockchain provides data integrity, transparency, and decentralized trusted authority management. The system is further enhanced by edge computing to alleviate latency and realize real-time processing performance. The experimental evaluation of the proposed framework is carried out under 100 IoT nodes to evaluate its performance. The results of the PQP_HS_DL provide a high classification accuracy (97.6%), and higher precision, recall, and F1-score compared to the existing techniques. The latency (72 ms) is lower, the throughput (285 transactions per second) is higher, and energy consumption (0.91) is also effective in the PQP_HS_DL framework for real-time applications of IoT. Security analysis shows that the entropy (0.98) is very high, and the attack probability (0.01) is very low. The framework uses lattice-based post-quantum cryptographic mechanisms, which are effective against any classical attacker as well as against existing quantum cryptanalytic methods, based on standard computational assumptions. Scalability analysis demonstrates that the proposed framework, evaluated with run on IoT networks with over 1000 nodes, exhibits significant performance.