Multi-cloud computing is becoming a prominent paradigm to improve scalability, flexibility, reliability and costeffectiveness by leveraging services from multiple cloud providers. But distributed resource management with strong security is a big challenge in multi-cloud scenarios, which are heterogeneous and dynamic. This review paper provides an all inclusive overview on various multi-cloud architectures, deployment models,resource allocation techniques, optimization methods, and security assurance mechanisms. It covers the major resource allocation strategies such as provisioning, scheduling, load balancing, resource scaling and intelligent optimization through machine learning and metaheuristicalgorithms to optimize resource utilization and Quality of Service (QoS). Additionally, the article delves into significant security methods for protecting decentralized cloud systems, including authentication, authorization, encryption, intrusion detection, trust management, and zero-trust designs. Also, through the comparison of the most recent literature, the current research trends, challenges and limitations for optimizing resources while keeping security in mind are pointed out. According to the review, combining AI-powered optimisation with sophisticated security frameworks has the potential to enhance the performance, resilience and reliability of multi-cloud environments. Last but not least, the paper outlines future research avenues for explainable AI, federated learning, blockchain-based trust management, energy-efficient resource allocation, and autonomous cloud orchestration to enable secure, scalable, and sustainable next-generation multi cloud computing environments.
Abstract This study explores the role of Artificial Intelligence (AI) in transforming agricultural supply chain management in Bangladesh through a systematic comparative analysis of existing literature, institutional reports, and global case studies. AI technologies including predictive analytics, machine learning, blockchain, and precision agriculture are examined for their potential to address longstanding inefficiencies in Bangladesh’s agri-supply chain. The study finds that AI-driven demand forecasting models using LSTM and ARIMA achieved 89–92% crop yield prediction accuracy, representing a 37% improvement over traditional methods. Smart warehousing systems reduced operational costs by 25% and increased order processing speed by 40%, while blockchain integration cut payment cycles from 15 days to 2.3 days and increased smallholder farmer incomes by 22–25%. Precision agriculture technologies achieved 25% yield growth with 15–20% water savings and 30% fertilizer efficiency gains. Despite these promising outcomes, Bangladesh’s AI adoption rate remains at only 18%, significantly behind India (35%) and Vietnam (28%), primarily due to insufficient infrastructure, lack of digital literacy, and high implementation costs. The study proposes targeted policy interventions including IoT subsidies, farmer training programs, and public-private partnerships to enable inclusive and sustainable AI integration across Bangladesh’s agricultural sector.
While much has been written about the volatility of digital assets, academic scholarship has largely overlooked how blockchain technologies have been adopted and reimagined by LGBTQ+ communities. This article addresses that gap through a digital ethnography of queer NFT communities active during the crypto craze of 2022, combining online participant observation with semi-structured interviews. Drawing on José Esteban Muñoz’s concept of queer futurity, it examines how queer users imagined blockchain as a speculative platform for alternative economic and social possibility—despite the financial risks embedded in the technology’s libertarian and capitalist structures. The article interrogates the utopian rhetoric of inclusion, decentralisation, and wealth redistribution that was deployed within these communities to justify their interest in and holdings of non-fungible tokens (NFTs) and cryptocurrency. Queer leaders leveraged the blockchain to foster inclusive digital communities and promote wealth circulation amongst LGBTQ+ individuals, while community members embraced the technology as a risky opportunity for queer economic mobility. The article positions blockchain as a contested site where competing futurities collide—offering the illusion of liberation and the reproduction of existing inequalities. It argues that while queer users sought to make the blockchain ‘queer from the start,’ their efforts were ultimately constrained by the capitalist logics that underpin the technology.
Muhammad Farooq Shaikh, S. Hamza Hassan, Jawwad Shamsi, Alessia Maccaro · 5 authors
Background and objective The integration of blockchain and digital twin (DT) technologies is increasingly recognised as a promising approach for improving healthcare data integrity, interoperability, privacy, and clinical decision support. While digital twins enable dynamic patient modelling and predictive healthcare applications, blockchain provides secure data governance through decentralised trust, auditability, and access control. However, existing research remains fragmented, with limited synthesis of the architectural integration, regulatory readiness, ethical governance, and interoperability of blockchain-enabled healthcare digital twin systems. This systematic scoping review addresses these gaps by providing a comprehensive architectural and compliance-oriented analysis of the current evidence. Methods A systematic scoping review was conducted following PRISMA 2020 guidelines using Scopus, PubMed, and Web of Science. From 148 identified records, 55 eligible studies published between 2020 and 2025 were included after duplicate removal and eligibility screening. Data were extracted on digital twin functionality, blockchain architecture, healthcare application domains, consensus mechanisms, privacy-preserving strategies, and regulatory and ethical alignment. Structured Python-based visual mapping and comparative analyses were performed to identify architectural, governance, and compliance patterns across the literature. Results The findings demonstrate that blockchain is predominantly employed to provide access control, audit logging, data integrity, consent management, and secure data provenance within healthcare digital twin ecosystems. Patient-level and EHR-centred digital twins represented the most mature application areas, whereas cross-domain and infrastructure-level frameworks dominated early architectural exploration. The review identifies recurring compliance-oriented architectural patterns while revealing substantial gaps in clinically validated deployments, interoperability with established healthcare standards, decentralised governance models, and formal implementation of GDPR- and HIPAA-compliant engineering practices. Comparative heatmap analyses further highlight the uneven maturity of ethical governance and regulatory integration across blockchain functionalities. Conclusion This review provides the first comprehensive compliance-oriented architectural synthesis of blockchain-enabled healthcare digital twin systems by integrating technical architecture, regulatory readiness, ethical governance, and privacy-preserving design patterns within a unified analytical framework. The proposed architectural mapping identifies critical research gaps in interoperability, governance engineering, consensus optimisation, and real-world clinical validation, providing a foundation for the development of trustworthy, GDPR/HIPAA-aligned, FHIR-compatible, and clinically interoperable healthcare digital twin ecosystems.
The use of a wireless sensor network is increasingly supporting e-governance functions such as municipal utility monitoring, environmental monitoring, grievance-based field reporting, and smart public service delivery. Most wireless sensor network architectures rely on a gateway or database. However, this introduces vulnerabilities to data integrity, node accountability, and auditability. This study examines transparency through a blockchain-enabled WSN architecture for e-governance. The study applies a reproducible Python-based Monte Carlo simulation with a fixed random seed, five node densities, three architectural scenarios, and 450 observations. The scenarios that are compared in this work are a normal WSN, a centralized secure WSN, and a permissioned blockchain-enabled WSN with smart-contract-based identity registration, hash-linked data records, trust scoring, and tamper verification. Descriptive statistics, one-way ANOVA, Welch t-tests, Pearson correlation, and multiple linear regression analysis. The blockchain-assisted WSN, as evidenced by the simulation findings of our project, produced the highest mean data integrity score, tampering detection rate, trust score, malicious node detection rate, and packet delivery ratio. The architecture also improved the composite service efficiency index relative to the conventional baseline, even though it introduced higher latency, transaction confirmation time, and energy consumption. The research indicates that the permissioned blockchain can enhance public-sector WSN transparency with edge aggregation and lightweight cryptographic operations along with carefully tuned endorsement rules. The methods presented in this study allow for scrutiny of secure WSN designs tailored for e-governance.
The development of blockchain technology has given rise to the Decentralized Autonomous Organization (DAO), a new business organizational model that operates through smart contracts in a decentralized manner, without a conventional management structure. The existence of DAOs has not been accommodated in the Indonesian corporate legal system, creating a legal vacuum regarding legal subject status, accountability, legal standing, taxation, and dispute resolution. This study aims to analyze the characteristics of DAOs from a corporate law perspective and the urgency of convergence between corporate law and blockchain technology in its regulation in Indonesia. The study employs a normative juridical method with statutory, conceptual, and comparative approaches. The results indicate the need for regulations that recognize and regulate DAOs as digital business entities to achieve legal certainty, legal protection, and a sustainable digital investment climate.
The rapid advancement of artificial intelligence (AI) and blockchain technologies has fundamentally transformed the normative foundations, authority structures, and legitimacy of contemporary legal systems. While these technologies are commonly portrayed as instruments for enhancing efficiency and legal certainty, their increasing integration into legal decision-making raises profound philosophical questions concerning the nature of law, justice, and human agency. This article critically examines how AI and blockchain reshape legal normativity through the lens of legal philosophy. Employing a normative juridical methodology supported by conceptual and philosophical approaches, the study analyzes the implications of algorithmic decision-making and decentralized technological infrastructures for the evolution of legal authority. The findings demonstrate a paradigmatic shift from human-centered normative reasoning toward computational rationality grounded in algorithmic logic. AI replaces interpretative legal reasoning with probabilistic prediction, privileging statistical inference over moral deliberation. Simultaneously, blockchain institutionalizes automated legal enforcement through smart contracts, thereby minimizing interpretative discretion and limiting the contextual flexibility traditionally required to achieve substantive justice. These developments contribute to the emergence of what this article conceptualizes as post-human legal normativity, in which legal authority increasingly resides within technological systems rather than human reasoning and institutional judgment. The study argues that this transformation generates significant challenges to justice, transparency, accountability, and democratic legitimacy. The growing reliance on algorithmic authority risks reducing law to a technical mechanism detached from its ethical and normative foundations. Consequently, the philosophy of law must be reconstructed to reaffirm the centrality of human agency in legal governance and to ensure that emerging technologies function as instruments serving legal values rather than autonomous sources of legal authority.
Transformer-based detectors for Solidity smart contracts almost universally encode a contract within a single 512-token window, then attribute performance differences to the choice of pre-trained encoder. We show this attribution is misplaced. On DIVE-25 (22,330 deployed contracts, eight DASP categories, multi-label at 2.46 labels per contract) the median contract occupies 2,994 sub-word tokens and only 5.48% fit a single window. We segment each contract at top-level declaration boundaries, pack the segments greedily into at most 24 chunks of 510 tokens for an effective context of 12,240 tokens covering 98.25% of the corpus, and recombine the chunk representations with a bidirectional LSTM under additive attention. Holding preprocessing, chunk budget, pooling, aggregator, loss, schedule, seeds and split identical, the extended context is worth +0.1038 micro-F1 and +0.1722 macro-F1 over single-window truncation, roughly four times the benefit of the best available encoder. The loss under truncation is markedly uneven: Front Running falls by 0.294 and Time manipulation by 0.262, while Access Control, whose indicators sit near the top of a file, loses 0.010. Fifty-one structural measurements and a five-relation contract graph enter the classifier through per-class gates initialised at σ(−4) ≈ 0.018, so any contribution must be learned; both open, and the resulting gain is 1.9 times larger on categories below 900 test instances. Under family-aware leakage-controlled partitioning the complete system reaches 0.8435 micro-F1 and0.7775 macro-F1, with the fusion gain significant under a paired bootstrap (macro-F1 +0.0173, 95% CI [+0.0113, +0.0236]). We report every result additionally on a twin-free test subset from which the 39.58% of test contracts sharing a structural twin with training are removed. Finally, evaluated against human-verified exploitability judgements the detector scores 0.455 mean AUC, below a baseline built from contract size and compiler version alone (0.735), bounding what any detector trained on analyser consensus can be claimed to do.
Land ownership management is a critical administrative process that requires secure record maintenance, transparent ownership verification, and efficient property transfer mechanisms. Conventional land registry systems primarily depend on centralized databases and paper-based documentation, making them susceptible to document forgery, unauthorized modifications, duplicate ownership claims, lengthy verification procedures, and administrative inefficiencies. These limitations often result in ownership disputes, reduced public trust, and delays in property transactions. This paper presents a Blockchain-Enabled Secure Land Registry Framework that leverages blockchain technology to establish a decentralized, transparent, and tamper-resistant platform for land registration and ownership management. The proposed system integrates a React.js-based user interface with a Node.js and Express.js backend, while Firebase Authentication and Firebase Firestore manage user authentication, supporting documents, and application data. Ethereum smart contracts developed using Solidity are employed to securely record land registration, government verification, and ownership transfer transactions on the blockchain, with Ganache serving as the blockchain testing environment. Every approved transaction generates a unique blockchain transaction hash, enabling secure verification, complete traceability, and immutable ownership history. The hybrid architecture combines the scalability of cloud-based data management with the integrity of blockchain technology to ensure efficient record retrieval while preventing unauthorized alterations. The implemented framework demonstrates secure land registration, transparent ownership transfer, simplified government verification, and reliable auditability with minimal operational complexity. The proposed solution provides a scalable and cost-effective approach for modern digital land administration and establishes a strong foundation for future integration with national land registries, electronic identity verification, GIS-based property mapping, and mobile-enabled citizen services.
Secure and transparent attendance management has become increasingly important in educational institutions as conventional attendance systems often face challenges such as proxy attendance, unauthorized record modification, and limited traceability. Most existing solutions rely on centralized databases, making them susceptible to data tampering, accidental loss, and single-point failures. This paper presents a Blockchain-Based Attendance Management System that leverages blockchain technology to provide a decentralized and immutable mechanism for recording and verifying attendance information. The proposed framework integrates a React.js-based user interface with a Node.js and Express.js backend, while Firebase Authentication and Firestore manage user authentication and application data. Attendance records are securely stored through Ethereum smart contracts executed on the Ganache blockchain network, with transaction hashes linked to Firebase for efficient retrieval and verification. This hybrid architecture combines the scalability of cloud-based data management with the integrity and transparency of blockchain technology. Once attendance is recorded, the information cannot be altered without detection, ensuring reliable auditability and improved trust among students, faculty members, and administrators. The implemented system demonstrates secure attendance recording, fast verification, and efficient transaction management while reducing the possibility of record manipulation. The proposed solution offers a practical, scalable, and cost-effective approach for modern attendance management and provides a strong foundation for future enhancements such as biometric authentication, QR code-based attendance, and cloud-enabled blockchain deployment.
The study investigated digital currency and blockchain technology in the 21st century financial ecosystem. The empirical study adopted a descriptive survey design. A questionnaire was used for data collection in a sample size of 121 selected randomly from the staff and students of Abia State Polytechnic, Aba. The data collected from the respondents were analyzed with the frequency distribution table and chi-square (x2 ) statistical technique. The findings revealed the imperativeness of digital currency and blockchain technology in the 21st century financial ecosystem. In other words, digital currency and blockchain technology has significant effect with financial ecosystem. The study, therefore, recommended among others that Central bank of Nigeria, legislators and financial stakeholders should collaborate to establish compliance standards and best practices for digital currency and blockchain integration in financial ecosystem. These standards should ensure that digital currency algorithms and blockchain technology conform with regulatory requirements and ethical principles, while promoting transparency and accountability.
Hadeer Khayoon Ashour, Noor Salah Alramadan, Hamid Mohsin Jadah
There is growing interest in using blockchain technology to overcome the flaws of legacy payment systems and banking operations, few empirical efforts have examined the possible use of blockchain by large institutions. The study examines how blockchain is changing the payment systems and banking services with a focus on Citigroup (Citi) and various Citi blockchain projects, specifically Citi Token Services. The study aims to assess the impact of blockchain’s adoption on efficiency, cost reduction, customer confidence and service accessibility. A quantitative research study was conducted in a longitudinal design, and data were analysed using multiple linear regression in the SPSS program from 2020–2024 to check the relationship between variables. The results indicate that blockchain implementation offers considerable transaction speed, operational and transactional cost reduction (up to 80 percent), increased customer trust and broader service access with 24/7 transactions. The regression model explains 51.9 percent of the variance in performance. Although promising, blockchain for banking is still in its infancy and facing a variety of challenges that need to be solved for wider application, such as scalability, regulatory compliance, and integration with existing systems.
The rapid growth of digital education and online recruitment has significantly increased the demand for reliable academic credential verification. Conventional certificate verification methods are often centralized, time-consuming, and susceptible to document forgery, unauthorized modification, and administrative delays. To address these challenges, this paper presents a Blockchain-Enabled Decentralized Framework for Secure Academic Certificate Issuance and Real-Time Verification. The proposed framework utilizes Ethereum blockchain technology through Solidity smart contracts to establish an immutable and transparent repository of certificate records, ensuring that issued credentials cannot be altered without detection. A SHA-256 cryptographic hashing mechanism is employed to generate unique digital fingerprints for each certificate, while Firebase Authentication and Cloud Firestore provide secure identity management and efficient off-chain metadata storage. The user interface is developed using React.js, enabling educational institutions to issue certificates and allowing employers, universities, and other stakeholders to verify credentials instantly through a simple web-based platform. During verification, the system recomputes the certificate hash and compares it with the blockchain record to detect tampering and validate authenticity in real time. Experimental evaluation on a local Ethereum network demonstrates reliable certificate issuance, rapid verification with sub-second response times, secure transaction handling, and effective resistance against certificate forgery. The proposed framework enhances transparency, trust, and operational efficiency while minimizing manual verification efforts. Furthermore, its modular architecture facilitates future migration to public blockchain networks and decentralized storage platforms, making it suitable for scalable deployment across educational institutions and digital credential ecosystems.
Dodi Setiawan, Sri Sutjiningtyas, A. Eka Hermia Fitrianingsy, Ronald Naibaho · 5 authors
Blockchain consensus mechanisms are critical for ensuring security, efficiency, and scalability in decentralized networks. This study qualitatively examines ten widely used consensus algorithms—Proof of Work (PoW), Proof of Stake (PoS), Delegated PoS (DPoS), PBFT, Raft, Proof of Authority (PoA), Hybrid PoW/PoS, DAG/IOTA, Hashgraph, and Tendermint—within the research context of Bintan, Riau Islands, Indonesia. Performance was evaluated through literature review and simulated network observations, focusing on transaction throughput (TPS), latency, energy consumption, and network stability. Results indicate that DAG/IOTA and Hashgraph achieve the highest throughput with minimal latency, making them suitable for IoT and enterprise-scale applications. PoS and PoA offer energy-efficient alternatives, while PoW provides high security at the cost of high energy usage. Hybrid PoW/PoS demonstrates balanced performance across multiple metrics. Qualitative analysis highlights trade-offs among energy efficiency, throughput, latency, and decentralization. These findings provide practical guidance for selecting consensus mechanisms according to network requirements, operational constraints, and sustainability considerations, contributing a consolidated perspective on blockchain efficiency and scalability.
Pipelines that pair a large language model with a static analyzer, feeding findings back as repair instructions, appear throughout recent smart contract repair research. They rest on a rarely examined assumption: that the analyzer output serving as the oracle faithfully records what the analyzer found. I report three ways that assumption fails, identified during a four-contract instrument-validation exercise preceding a planned repair study. First, Mythril v0.24.8 can exit without reaching the analysis phase while returning exit status zero, empty standard error, and a findings array byte-identical to that of a genuinely clean scan; the failure is reported in a sibling JSON field that finding-extraction code has no reason to read. Second, 12 of 23 Slither findings in my validation set fell outside the high, medium, and low impact bands, so an unfiltered count measures a composite whose components may not behave alike under repair. Third, keying finding identity on source location breaks across repair rounds. On the one contract carried through three rounds, location-based keying inflated resolved findings from 7 to 12 and introduced findings from 2 to 7. The underlying instability is established in the warning-tracking literature; my contribution is its consequence for repair metrics, where it biases both transition counts upward and can confound comparison between methods producing differently sized .patches. I separately report an executed exploit showing a specification-level authorization defect that produced no high or medium impact finding. I propose calibration procedures for each hazard and release the harness, contracts, and raw analyzer output at doi:10.5281/zenodo.21586404.
Crypto currency is one of most interesting financial innovation of 21st century. Crypto currency trading not only involve financial literacy while trading but also there are psychological factors affecting the decision of traders. Keeping in view the psychological factors and investors’ decision, this research study is designed to investigate the complex interplay between psychological triggers and market dynamics in the cryptocurrency sector in Pakistan, specifically examining how these elements coalesce to drive investor behavior and market volatility. While traditional financial models often attribute asset fluctuations to technological or fundamental shifts, this study posits that cryptocurrency markets are fundamentally driven by human perception and emotional reactivity. Utilizing a quantitative methodological approach, data was collected from a sample of 175 experienced traders to analyze the impact of emotional states, market sentiment, and behavioral discipline on trading outcomes. The empirical results, derived through multiple linear regression analysis, reveal that the model possesses a high level of explanatory power, accounting for 56% of the variance in emotional trading behavior (R2=0.56R2=0.56). Market sentiment emerged as the primary determinant of impulsive trading (β=0.48β=0.48), demonstrating that external social cues often exert a stronger influence on decision-making than internal emotional states. Among specific psychological variables, Fear, Uncertainty, and Doubt (FUD) were identified as the most significant predictors of rash choices (β=0.34β=0.34), while the Fear of Missing Out (FOMO) also demonstrated a substantial, though secondary, effect (β=0.21β=0.21). Conversely, the study found that trading experience and the application of systematic strategies serve as vital moderating factors that decrease emotional reactivity and enhance behavioral stability (β=−0.19β=−0.19). The findings contribute to the fields of behavioral finance and digital economics by illustrating that the volatility inherent in digital assets is a systemic byproduct of individual psychological biases aggregated through digital narratives. The research concludes that achieving a sustainable financial ecosystem requires moving beyond purely technical regulations. Instead, it advocates for the implementation of behaviorally-informed safeguards, such as algorithmic "cooling-off" periods and sentiment-aware trading tools, to mitigate the risks associated with reactive investing. Ultimately, this work provides a blueprint for a more resilient digital financial future by prioritizing human factors in market governance.
The modern energy transition has outpaced the control and optimization frameworks built to govern it. As power and energy systems fragment into webs of renewable generators, storage operators, flexible loads, and carbon-constrained firms, the deterministic, single-optimizer models that once sufficed buckle against nonlinearity, bounded rationality, and strategic conflict among parties who learn and revise as they go. Evolutionary game theory (EGT), which traces how strategies propagate through populations by imitation and selection rather than instantaneous optimization, offers a route through this difficulty—one this review develops across three scales of low-carbon coordination central to cleaner production: enterprise-level industrial symbiosis, system-level smart energy operation, and market-level carbon governance. We synthesize three decades of theory alongside the recent fusion of EGT with artificial intelligence, where deep reinforcement learning approximates high-dimensional payoffs, federated learning lets rival firms co-train models without surrendering proprietary data, and blockchain underwrites decentralized mechanism execution. The synthesis is accompanied by two illustrative numerical case studies, constructed for this review rather than drawn from the surveyed literature, whose quantitative outputs are reported below as demonstrations of modeled behavior rather than as empirical measurements. In the first of these, cooperative emergence in industrial symbiosis hinges on critical thresholds that travel from 0.15 to 0.75 as subsidies and transaction costs vary, with anchor-enterprise targeting accelerating cooperation 2.4-fold while cutting outcome variance 3-fold. In smart energy coordination, AI-enhanced learning buys 32 to 41% faster convergence, yet pays 25 to 39% larger oscillations—a speed–stability tension whose resolution lives in a narrow learning-rate band near 0.08 to 0.12, outside which either sluggishness or instability takes hold. Carbon-market behavior turns on price thresholds: emitters switch abruptly from buying quotas toward investing in abatement once the clearing price clears firm-specific triggers, a discrete state switch that smooth equilibrium analysis misses entirely. Across all three domains, fragmented data, path dependence, and regime-switching dynamics recur as the binding constraints on modeling and on governance alike. Four mechanisms prove invariant to scale—the decisive weight of initial conditions, the catalytic leverage of well-positioned anchor agents, the equilibrium-shaping force of institutional design, and the computational reach added by AI integration—which suggests that insight earned in one domain transfers to the others. We close by mapping open problems in heterogeneity modeling, verification under deep uncertainty, and the still-unrealized coupling of digital twins with privacy-preserving learning. EGT emerges not as retrospective description but as prospective guidance for the cooperative transitions on which credible decarbonization depends.
The subject matter of the article is the cryptographic integrity of digital authentication systems facing quantum computing threats, specifically focusing on post-quantum alternatives and efficient authenticated data structures. The goal is to design and formally analyze VERKLE-FRI—a hybrid architecture synthesizing Verkle tree proof-size reduction with FRI-based quantum-resistant commitments, establishing a scalable, stateless, and quantum-secure framework. The tasks are: analyze limitations of hash-based signatures and Merkle trees; evaluate polynomial commitment schemes (KZG, Bulletproofs, FRI, lattice-based); propose a hybrid Verkle-FRI design; develop a formal security proof against classical and quantum adversaries; execute complexity analysis with concrete implementation parameters. The methods used are: theoretical cryptographic analysis, formal security modeling via reductionist proofs, algebraic methods over finite fields, polynomial interpolation, random oracle model, FRI protocol with DEEP-FRI optimization, Merkle trees, vector commitments, and asymptotic complexity analysis. The following results were achieved: a novel architecture where Verkle node vectors are polynomial-encoded, committed via Merkle trees over FRI codewords, and verified through FRI with out-of-domain sampling. A formal proof establishes λ-bit quantum security using 2λ-bit hash functions. Complexity yields proof size O(λ log² N), prover time O(λ N log N), and verifier time O(λ log N). Concrete 128-bit quantum parameters include SHA3-512, field size ≈2²⁵⁵, branching factor 256, and 128 FRI rounds, achieving soundness error ≤2⁻¹²⁷. For a concrete benchmark authenticating 2²⁶ elements, a traditional Merkle proof requires ≈0.8 KB, whereas our VERKLE-FRI proof requires ≈180 KB. While larger, this provides quantum resistance and eliminates the trusted setup, a critical trade-off for long-term security. Conclusions. Scientific novelty consists in: 1) the first hybrid Verkle-FRI architecture replacing pairing-based assumptions with hash-based proximity testing; 2) a formal security proof reducing security to hash collision resistance and FRI soundness; 3) quantified efficiency-security trade-offs; 4) a viable pathway for quantum-resistant infrastructure in blockchains, software distribution, and government communications.
Open access
Cryptographic Implementations and Security
Cryptography and Data Security
Physical Unclonable Functions (PUFs) and Hardware Security
Initial Coin Offerings (ICOs) have emerged as an innovative mechanism for raising capital, particularly for blockchain-based projects. However, the lack of regulatory oversight and the prevalence of low-quality information raise important questions about what truly drives ICO success. While existing literature focuses predominantly on technical and signalling variables, the role of investor decision-making remains theoretically underdeveloped and empirically underexplored. This paper addresses this gap by pursuing two objectives. First, we identify the drivers of ICO success using a probit model applied to an original sample of 535 ICOs conducted between January 2016 and May 2021. Second, we investigate investor decision-making patterns using a novel dataset of 200 active crypto-forum participants over the same period. Our results have three main findings, though with modest statistical strength than initially estimated. (I) Marketing channels are the most consistent predictor of ICO success across the sample period, clearing conventional significance thresholds only in the pooled sample (z = 1.90, p<0.10), with each additional channel raising the probability of soft-cap achievement by approximately 1.0 percentage point. (II) Team presentation and video presentation show no meaningful influence on success in any period. (III) Whitepaper availability is not statistically significant even in pooled sample, reinforcing rather than qualifying its irrelevance as a predictor; the number of accepted cryptocurrency price speculation rather than project fundamentals, consistent with mood and sentiment dominating information-based decision making in ICO markets, though this finding should be read alongside the data limitations discussed in 3.B. These findings contribute to the behavioural finance literature by providing an operational definition of ‘investor mood’ and demonstrating its empirical relevance in crypto markets. We conclude that understanding investor mood is not a secondary question but a necessary complement to technical analysis of ICO success.
This paper presents a threshold-cryptographic architecture for reducing the risk of premature leakage of digital examination papers during the interval between question-paper finalization and examination administration. The proposed design separates the data path from the control path. Examination content is encrypted using a fresh AES-256-GCM key, while the key is protected through envelope encryption under a key-release service. The capability to release that key is distributed using (k,n)-Shamir secret sharing across independent custodians, preventing any single custodian from unilaterally authorizing early release. At the scheduled release time, a quorum-based time authority provides an independently attested timestamp. Once the required time quorum and custodian threshold are satisfied, the key-release service reconstructs its private key within an HSM boundary, unwraps the examination key, and derives recipient-specific keys for individual examination centers. These keys are separately wrapped under each center's registered public key, limiting the impact of a compromise at any single examination center. The paper presents an actor and trust model, an explicit adversary model, a step-by-step release protocol, a threat-to-control security analysis, and a qualitative comparison with physical custody, blockchain-anchored distribution, and time-lock-puzzle-based timed-release cryptography. It also explicitly discusses residual risks, including custodian collusion, post-decryption optical or physical exfiltration, hardware and supply-chain trust, and compromise of the time-authority quorum. The architecture is presented as a research design rather than a claim of unconditional leak prevention. Future work includes implementing a prototype, evaluating quantitative performance, replacing reconstruct-and-zeroize key handling with threshold decryption, evaluating post-quantum key-encapsulation mechanisms, and conducting a formal mechanized security proof.
Open access
2 source records
Chaos-based Image/Signal Encryption
Physical Unclonable Functions (PUFs) and Hardware Security
The rapid diffusion of digital technologies has fundamentally reshaped the way organizations generate and report financial and non-financial information, challenging traditional audit approaches that rely on manual and sample-based procedures. Building on this context, this paper aimed to provide a comprehensive synthesis of empirical evidence regarding the impact of digital technologies on auditing and to identify the key factors influencing their adoption across internal, external, and public sector audit functions during the 2015–2026 period. Using a qualitative descriptive design and a systematic literature review guided by the PICOC framework and PRISMA protocol, 33 relevant articles indexed in Scopus were selected from an initial pool of 959 publications. The findings showed that the use of various technologies, including computer-assisted audit techniques (CAATs), audit analytics, big data, artificial intelligence, robotic process automation, blockchain, and process mining, generally enhanced the effectiveness and efficiency of audit procedures, strengthened internal controls, and reduced errors and financial statement restatements, while simultaneously repositioning auditors as more strategic and data-driven partners. At the same time, the success of digital audit transformation was strongly influenced by technological infrastructure, data governance and security, organizational capabilities, leadership support, regulatory environments, and auditors’ individual competencies, indicating that digitalization was neither a neutral nor an automatic process. This study provides practical implications for audit firms, internal audit units, supreme audit institutions, and regulators in developing more targeted and sustainable digital audit strategies, while also proposing future research directions concerning the organizational and institutional dynamics of digital auditing.