Yield-Aggregatoren automatisieren den Prozess des Yield-Farming im Bereich des Decentralized Finance (DeFi), indem sie Nutzerkapital bündeln und über verschiedene Protokolle hinweg einsetzen, um Renditen zu optimieren. Aufgrund ihrer hohen Komplexität sind ihre Funktionsweisen jedoch schwer nachzuvollziehen, und die Forschung zu ihren internen Mechanismen sowie den Interaktionen mit anderen Protokollen ist bislang begrenzt. Diese Arbeit adressiert diese Forschungslücke durch die Analyse zweier Ethereum-basierter Yield-Aggregatoren: Yearn Finance und Cian Yield Layer. Hierzu wurden Blockchain-Daten über einen Zeitraum von einem Jahr (4. Mai 2024 bis 3. Mai 2025) erhoben und ausgewertet, bestehend aus 2.459 Yearn-Transaktionen mit 5.575 Token-Transfers sowie 921 Cian-Transaktionen mit 1.963 Token-Transfers. Die Arbeit kombiniert eine operative Analyse, eine Netzwerkanalyse der Kapitalflüsse und einen Vergleich der Plattformmerkmale. Die Ergebnisse zeigen unterschiedliche Strategien: Yearn investiert Kapital überwiegend in Lending-Protokolle, indem es Liquidität zur Verfügung stellt, während Cian auf gehebeltes, rekursives Staking unter Einsatz von Flash-Loans setzt, um Restaking-Erträge zu erhöhen. Yearn hat eine breite Nutzerbasis mit vergleichsweise kleinen Einzeltransaktionen, während Cian eine kleinere Nutzerbasis besitzt, die von einem höheren Anteil großer Einzahlungen geprägt ist. Auf Grundlage der Analyse wurde ein konzeptionelles Modell entwickelt, das aus zwei miteinander verbundenen Lebenszyklen besteht: dem User-Lifecycle (Einzahlungen, Halteperiode, Auszahlungen) und dem Strategy-Management-Lifecycle (Kapitalallokation, Strategieausführung, Umschichtung). Dieses Modell erfasst die grundlegenden ökonomischen Funktionen von Yield-Aggregatoren unabhängig von ihrer technischen Implementierung. Die Arbeit liefert empirische Einblicke in die Funktionsweise von Yield-Aggregatoren, identifiziert DeFi-Protokolle als Investitionsziele und stellt ein konzeptionelles Modell zum Verständnis der Mechanismen von Yield-Aggregatoren vor.
Machine Law Engine (MLE) v1.2.0 presents a formal computational architecture that reconceives regulatory compliance from a retrospective, documentary discipline into a pre-emptive, cryptographically enforced state property. Where classical GRC tooling observes violations after they occur, the MLE enforces legal constraints before execution — making non-compliant operations computationally impossible rather than merely detectable. The architecture introduces three original contributions to the field of computational law and applied cryptography: (1) The Admissibility Vector — a four-dimensional formal scoring function (authority α, evidence ε, context γ, transition legality τ) that evaluates every regulated operation at execution time against all applicable legal rules. The collapse axiom τ=0 → Φ=0 produces terminal refusals for legally impossible state transitions that cannot be overridden by any combination of authority or evidence. (2) The Challenger Provenance Architecture — a novel mechanism, without precedent in published GRC frameworks, that enforces structural independence of AI-assisted compliance reasoning. If a challenger input cannot demonstrate cryptographic divergence (CPD ≥ 0.70, path_overlap ≤ 0.20) from the primary reasoning path, the gate cannot achieve full institutional binding — operationalising DORA Art.15, EU AI Act Art.9(9), and BCBS 239 Principle 11 as cryptographic invariants rather than policy obligations. (3) The Seven Formal Invariants — hard computational constraints governing the MLE's correctness properties, with mathematical predicates, three-tier runtime monitoring (write-time, scheduled, continuous), and automated violation response protocols including cryptographically evidence-hashed remediation workflows. The reference implementation integrates: four hardware TEE providers (AWS Nitro Enclave, Azure Confidential Computing, Intel SGX/TDX, AMD SEV-SNP) with PCR register semantic attestation; a post-quantum cryptographic stack fully standardised under NIST FIPS 203/204/205 (CRYSTALS-Kyber-1024, CRYSTALS-Dilithium-3, SPHINCS+) providing 30-year evidence integrity against harvest-now-decrypt-later attacks; four PLONK-based Zero-Knowledge proof circuits on BLS12-381 (128-bit soundness) resolving privacy-compliance paradoxes for OFAC sanctions screening, FinCEN BSA threshold verification, DORA Art.28 vendor certification, and GDPR right-to-erasure evidence chains; a bi-temporal append-only ledger with DORA Art.11 automated retro-simulation; a seven-stage NLP-to-enforcement-code compilation pipeline with Kyber-1024 tamper detection and dual-approval protocol; a multi-framework conflict engine covering six active cross-regulatory conflict pairs (GDPR × FINMA, GDPR × FinCEN BSA, DORA × NIS2, EU AI Act × GDPR, eIDAS 2 × CCPA) with five deterministic resolution strategies; and nine Interactive Verification Layer modules enabling complete live regulatory demonstration in 35 minutes without preparation. Regulatory framework coverage spans 17 frameworks across EU, US, CH, and UK jurisdictions including DORA, GDPR, NIS2, EU AI Act, eIDAS 2, FINMA Circ.2023/1, BaFin MaRisk, FinCEN BSA, OFAC/CAATSA, FATCA, CRS, ISO 27001:2022, and SOC 2. Evidence export targets eight regulatory authorities (EBA, EDPB, ENISA, FINMA, BaFin, FCA, SEC, FinCEN) in authority-native formats (XBRL, XML, BSA E-Filing) via Dilithium-3-signed, SPHINCS+-sealed bundles with direct API transmission. The system is currently deployed in production as of 31 March 2026. Invariant status at publication: 6/7 HOLDING · INV-5 WARNING (AMD SEV-SNP PCR2 drift, remediation active, resolution within 72 hours). Keywords: machine law, pre-emptive compliance enforcement, admissibility vector, post-quantum cryptography, trusted execution environment, zero-knowledge proofs, bi-temporal ledger, DORA, GDPR, EU AI Act, cryptographic compliance, challenger provenance, regulatory technology, GRC, hardware attestation, CRYSTALS-Kyber, CRYSTALS-Dilithium, SPHINCS+, PLONK License: CC BY 4.0 Version: 1.2.0 DOI: 10.5281/zenodo.immo.quickCore.1.2.0
Threshold transactions in Bitcoin is an effective solution for vulnerability of wallets to the loss or compromise of secret keys. It also enhances the applicability of Bitcoin to include use-cases that require partitioning the trust among a set of parties. Currently, the threshold transactions on Bitcoin expose the actual signers within the group of participants. This poses a threat of wallet hacks or theft targeting these signers. To address this issue of privacy, we propose a novel protocol to create threshold transaction using a combination of on-chain locking and off-chain proof of knowledge. As Bitcoin currently does not support verification of zero-knowledge schemes, the proposed protocol uses a Trusted Third Party ( TTP ) to verify the proofs off-chain. The trust on the third party is only limited to its service of signing on behalf of the users. The main contribution is the development and applicability of a m-out-of-N proof of partial knowledge that maintains the privacy of the signers both on-chain from the transaction verifiers and off-chain from the TTP and other signers as well. The protocol leverages Taproot’s spending path flexibility to incorporate dual spending capabilities and employs off-chain zero knowledge Σ-protocols to prove knowledge of private keys without disclosing their associated public keys. Experimental analysis demonstrates improved scalability and privacy than the mainstream threshold signature schemes for Bitcoin. A formal analysis demonstrates and establishes the security goals of the proposed mechanism.
The current methods don't meet the security and performance needs of Internet of Vehicles (IoV) apps, and they also don't give the end user a low-latency, secure edge-computing service at the same time, while in the context of vehicles. This study presents a blockchain-enabled edge computing architecture that employs Double Deep Q-Network (DDQN) for reinforcement learning and lightweight Practical Byzantine Fault Tolerance (PBFT) for consensus, aiming to simultaneously enhance latency, energy efficiency, and security. The containerised architecture uses Hyperledger Fabric with Kubernetes to efficiently manage micro-services and move tasks off of them. In urban, suburban, and highway settings, the framework consistently outperforms baseline algorithms, with a 30–45% improvement in end-to-end latency and a 55% reduction in energy use under moderate to heavy loads. The system finished more than 95% of its tasks while keeping block consensus times under 1.2 seconds at peak loads. The architecture also showed consistent performance with different levels of vehicle density and used zero-knowledge proofs with attribute-based security to protect data from cyber threats from bad actors. These findings indicate that the integration of DDQN and blockchain will mitigate security issues in the Internet of Vehicles (IoV) by enabling secure edge computing for future vehicular networks.
This study investigates the rising security vulnerabilities in decentralized finance (DeFi) platforms from both technical and operational perspectives. Through literature review, case studies, and a comparative platform analysis, the research identifies the root causes, user impacts, and mitigation strategies for common security issues. Prominent incidents such as Ronin Network, Poly Network, Mango Markets, and Curve Finance are examined in depth, while security strategies of major DeFi platforms such as Aave, Compound, Uniswap, and Synthetix are compared. The study also discusses the implications of new technological developments like Ethereum Layer-2 solutions, Zero-Knowledge rollups, and account abstraction mechanisms on DeFi security. Findings emphasize that achieving a sustainable DeFi ecosystem requires a holistic approach involving not only technical safeguards but also transparent governance, user education and robust audit processes.
The fast move toward sixth-generation (6G) distributed networks is making it possible to create highly dynamic, intelligent, and collaborative service environments for a wide range of use cases, including smart cities, autonomous systems, industrial IoT, immersive communication, and edge intelligence. But working together on a large scale in 6G environments comes with a lot of technical problems, such as the need for instant access to resources, coordinating different types of services, exchanging data that can grow, and making sure that security, trustworthiness, and privacy are all strong. Traditional centralised architectures have trouble meeting these needs because they have single points of failure, limited transparency, and problems with managing trust. Blockchain technology provides decentralisation, immutability, and the establishment of trust; however, its fundamental limitations in throughput and storage capacity impede its direct implementation in extensive 6G distributed collaboration systems. This paper presents a universal blockchain-based collaboration architecture specifically designed for 6G distributed networks, accompanied by an end-to-end collaboration mechanism aimed at delivering efficient, secure, and reliable resource-sharing functionalities. The proposed architecture combines service-oriented design ideas with adaptive blockchain improvements to get around problems with scalability. To address the throughput constraints of traditional blockchain systems, a service-oriented, capacity-adaptive blockchain sharding framework is proposed. In this framework, network nodes with different levels of consensus efficiency are dynamically split into different shards using a strategy that rates nodes based on their reputation. The assessment checks the performance of nodes by looking at things like their computational power, communication delay, reliability, and past behaviour. This is to make sure that shard formation is fair and reliable. Also, transactions are grouped by service type and sent to the right shards, which have the right level of consensus for the service. This service-aware transaction assignment makes sure that high-performance shards handle services that need to be processed quickly, while shards with moderate consensus capabilities handle services that don't need to be processed as quickly. This kind of adaptive alignment between service characteristics and shard performance greatly improves the overall throughput of the system and the efficiency of resource use. To make consensus even more efficient when workloads change, a load-sensitive Practical Byzantine Fault Tolerance (PBFT) mechanism is suggested for intra-shard consensus. The proposed load-aware enhancement dynamically changes consensus parameters based on shard load conditions, which is different from regular PBFT, which may slow down when there are a lot of transactions. This adaptive approach cuts down on communication overhead, makes the system more fault-tolerant, and keeps consensus performance stable even when many people are working together. So, the architecture makes sure that transactions are always valid while still meeting the ultra-low latency and high reliability needs of 6G apps. Along with throughput issues, storage scalability is still a big problem for blockchain-based systems because the ledger size keeps getting bigger. The paper proposes a hybrid storage policy that combines both on-chain and off-chain storage methods to get around this problem. To keep things immutable and trustworthy, important metadata, transaction proofs, and security-related records are kept on-chain. Large amounts of service data and information about sharing resources are kept off-chain using distributed storage solutions. Secure cryptographic connections between on-chain and off-chain parts make sure that data is accurate and can be verified without putting too much strain on the blockchain ledger. This mixed strategy greatly reduces the pressure on storage while keeping things clear and traceable. A lot of simulations are done to see if the proposed architecture and mechanisms are possible, can be scaled up, and will work better than other options. The results show that this new way of working together on a blockchain has a lot better throughput, less consensus latency, more balanced shard usage, and better storage efficiency than traditional blockchain-based collaboration models. Also, the proposed framework offers strong security guarantees and is resistant to bad behaviour in networks with different types of devices. In general, the suggested universal blockchain-based collaboration architecture is a scalable, secure, and adaptable way to make resource sharing in 6G distributed networks more efficient. The framework effectively solves blockchain scalability problems while also meeting the strict performance needs of next-generation distributed communication systems by combining service-aware sharding, load-sensitive consensus optimisation, and hybrid storage design. The results show that the architecture has the potential to be a key part of trustworthy and smart collaboration in future 6G ecosystems.
We present HyperVerITAS, a new zero-knowledge proof (ZKP) system for image provenance that enables scalable, efficient, and privacy-preserving verification of image transformations. HyperVerITAS builds upon the same minimal trust model as VerITAS (IEEE S&P '25), requiring trust only in the image source device, while treating the editing software as untrusted. Unlike VerITAS, which relies on FFT-intensive SNARKs and suffers from high memory overhead (up to 120 GB), HyperVerITAS leverages multilinear polynomial encodings over the Boolean hypercube to dramatically reduce both proving time and memory usage. Our design cleanly separates signature verification from image transformation, supports modular integration of multiple polynomial commitment schemes (including post-quantum constructions) and naturally extends to a wide range of affine image transformations. We implement HyperVerITAS with two distinct commitment schemes (Brakedown and multilinear KZG) and evaluate it on full-system pipelines involving cropping and grayscaling. On commodity hardware (Apple M3, 36 GB RAM), HyperVerITAS generates proofs for 33 MP images using only 27 GB of RAM and 6.6 minutes of proving time, whereas VerITAS fails to scale beyond 4 MP. These results establish HyperVerITAS as a practical and scalable ZKP system for secure and efficient image provenance.
Federated unlearning enables clients to withdraw their contributions from a global model.However, enabling clients to verify whether the server has honestly and effectively removed their contributions remains a critical challenge. To address this aspect, which has been largely overlooked in existing literature, a verification model based on zero-knowledge proofs was constructed, and a comprehensive framework for verifiable federated unlearning was proposed. Combined with a dynamically updated Merkle tree structure, a novel verifiable federated unlearning scheme was presented characterized by its zero-knowledge property. This allows for the efficient generation of cryptographic proofs for server unlearning operations while rigorously protecting the data privacy of other clients. We evaluate the effectiveness and computational overhead of the proposed scheme. Comparative experiments with Rivest-Shamir-Adleman (RSA) accumulator-based and Hash chain-based schemes demonstrate that, when the model parameter size reaches the order of <inline-formula><alternatives><math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><msup><mrow><mn mathvariant="normal">10</mn></mrow><mrow><mn mathvariant="normal">5</mn></mrow></msup></math><graphic specific-use="big" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/B6D6E598-14B1-468e-9A32-73199F9CD69E-M002.jpg"><?fx-imagestate width="4.23333359" height="2.53999996"?></graphic><graphic specific-use="small" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/B6D6E598-14B1-468e-9A32-73199F9CD69E-M002c.jpg"><?fx-imagestate width="4.23333359" height="2.53999996"?></graphic></alternatives></inline-formula>, the proposed scheme improves proof generation speed by approximately two orders of magnitude and verification speed by 13.2 times compared to the RSA-based scheme. Furthermore, it effectively avoids the scalability bottleneck of data linear growth in verification overhead inherent in Hash chain-based schemes.
As Distributed Ledger Technology and smart contracts continue to grow in popularity, there is increasing interest in developing more expressive programming abstractions for digital asset management, along with verification tools that ensure safety and correctness before deployment on blockchain platforms. Addressing this challenge, we introduce AlgoMove , a framework designed to improve smart contract development on the Algorand blockchain. While Algorand is widely recognized for its high performance, scalability, and secure consensus protocol, it still lacks high-level programming abstractions and strong language-based verification mechanisms. AlgoMove brings the Move language, renowned for its robust support for secure digital asset management, to the Algorand platform, adapting its abstractions to the underlying execution model. The result is a high-level, resource-oriented programming model that preserves the core principles of Move while adapting them to Algorand’s unique environment. We present a formal specification of AlgoMove and its encoding into TEAL, Algorand’s native assembly-level language, along with a proof of the soundness of this encoding. To demonstrate the practical value and expressive power of the framework, we provide a prototype implementation consisting of a Move-to-TEAL compilation system and an accompanying library for writing smart contracts. Beyond enhancing the Algorand smart contract ecosystem, AlgoMove is significant in its own right as part of a broader effort to bring advances in programming language theory and formal verification into the blockchain space. By balancing expressiveness, ease of use, and strong compile-time guarantees, we seek to meet the distinctive requirements of secure and reliable blockchain applications.
This paper examines the market maturation hypothesis in cryptocurrency markets through a three-stage analysis of the evolution of tail risk in Bitcoin (BTC) and Ethereum (ETH). Using daily closing prices from January 2015 to February 2026 for BTC (n = 4058) and November 2017 to February 2026 for ETH (n = 3015), we employ 365-day rolling windows—reflecting the continuous 24/7 operation of cryptocurrency markets—to trace the temporal dynamics of Value-at-Risk (VaR), Conditional Value-at-Risk (CVaR), and Maximum Drawdown (MDD). The empirical strategy combines (i) Newey–West trend tests on rolling risk metrics, (ii) regime-conditional analysis across market states (Bull, Bear, or Neutral) and volatility regimes (high/low uncertainty), and (iii) exceedance correlation analysis to capture asymmetric BTC–ETH tail dependence. The results are consistent with the market maturation hypothesis: all ten trend coefficients across both assets are statistically significant (p < 0.001), with linear time trends explaining up to 46.8% (BTC VaR1%) and 67.5% (ETH VaR1%) of variation in rolling tail risk. Sub-period comparisons confirm economically meaningful declines—BTC VaR1% fell by 22.0% and ETH VaR1% by 26.6% between the early and late subsamples. However, maturation is markedly asymmetric across uncertainty regimes: tail-risk reductions concentrate in low-uncertainty periods, whereas BTC MDD in high-uncertainty regimes shows no significant improvement (+1.0%, p = 0.176). Excess correlation analysis reveals a persistent and widening downside asymmetry (ρ− = 0.847 vs. ρ+ = 0.246 at the 90th percentile), with late-period upper-tail correlation turning negative (ρ+ = −0.175 at the 95th percentile), implying that portfolio diversification within the cryptocurrency asset class remains illusory during market stress. These findings carry direct implications for institutional risk management, stress-testing frameworks, and prudential regulation of digital assets.
This research paper provides an exhaustive and multi-dimensional analysis of the relationship between financial literacy and the investment behaviors of the teenage demographic (ages 13–19). In the contemporary era, characterized by the "fintech revolution" and the ubiquitous nature of digital assets, traditional barriers to entry in financial markets have largely disintegrated. Consequently, adolescents are now engaging with highly complex and volatile financial instruments, including fractional equities, cryptocurrencies, and non-fungible tokens (NFTs), often before they have attained a basic understanding of economic principles. This study identifies a critical "literacy-participation gap" that exposes young investors to unprecedented risks. Utilizing a qualitative-descriptive meta-synthesis, the paper integrates perspectives from behavioral economics, social learning theory, and adolescent neurobiology to evaluate how varying levels of financial knowledge influence risk perception, asset selection, and long-term financial health. The findings suggest that while high levels of financial literacy correlate with diversified portfolios and risk-mitigation strategies, the "gamified" architecture of modern trading platforms and the influence of social media "finfluencers" often override rational decisionmaking processes. The paper concludes with an urgent call for a paradigm shift in financial pedagogy, advocating for the integration of digital media literacy and behavioral psychology into standard secondary education to foster a more resilient generation of investors.
The civil law consequences of the use of non-fungible tokens (NFTs) in mass multiplayer online role-playing games (MMORPGs) as a special type of multimedia products are studied. The analysis focuses on the social relations arising from the tokenization of in-game objects, the issuance, circulation, acquisition, and use of NFTs within the gaming environment, as well as the determination of the rights of participants in these relations. Special attention is given to the delineation of rights to the token itself as a record in a distributed ledger, rights to the associated digital object, exclusive rights to elements of the multimedia product, and the obligations of users resulting from licensing and user agreements. The study also addresses the place of NFTs in the system of property rights, their relationships with digital rights, virtual assets, and other civil law constructs. Method, methodology of the research. The formal-legal, systemic, comparative-legal, and doctrinal methods have been employed. Provisions of the Civil Code of the Russian Federation, regulations on digital financial assets, academic literature, user agreements of gaming projects, and foreign approaches have been analyzed. Special attention has been paid to licensing constructs and the differentiation of obligation and intellectual rights. It is advanced by the author that NFTs within MMORPGs should not be automatically classified as digital rights in the sense of Article 141.1 of the Civil Code of the Russian Federation, nor as digital financial assets, nor as things in the classical sense. It is proposed by the author to regard them as a special virtual asset— a unique record in a distributed ledger, certifying control over the token by a particular entity and capable of being linked with a digital object or a contractually defined scope of possibilities for its use. It is shown that acquiring an NFT does not in itself lead to the transfer of exclusive rights to the associated content, and the scope of user rights is primarily determined by user and licensing agreements. The results can be used for the legal qualification of in-game digital assets, the preparation of contractual models, judicial arguments, scientific qualification of similar objects, and the improvement of multimedia product regulation.
Two accounts of reality compete for authority. What measurement produces and what experience carries. When they conflict, and they do, in doctor's offices, classrooms, courtrooms, boardrooms, everywhere consequence is at stake, the demand has always been to choose. Pick the data or pick the feeling. The choice was presented as epistemological necessity. It was not. It was the consequence of a missing category. This whitepaper names the missing category. Proxy Knowledge, computational pattern recognition at scale, producing representations of both scientific and experiential domains while accessing neither, has been operating without a name since the first algorithm was trusted to govern what it could only approximate. Without a name it could not be seen. Without being seen it could not be refused. It occupied the space between what you felt and what you were told, wearing the authority of science while carrying none of its standing, and the binary it forced was never real. Three knowledge types, not two. Scientific Knowledge holds standing through methodological submission to independent verification. Lived Knowledge holds standing through first-person access and consequence. Proxy Knowledge holds utility without authority basis — it informs, it cannot govern. The formula that integrates them: Sovereign Knowledge = Recognizing(Scientific Knowledge, Lived Knowledge; Proxy Knowledge). The semicolon is not notational. It is protective. Proxy surrounds both standing-bearing inputs at institutional scale. Without structural placement, the conflation reconstitutes inside the integration itself. Two proofs establish the formula's necessity. The Sovereign Knowledge Proof derives that legitimate integrated authority requires both Scientific and Lived Knowledge — each bounded, each insufficient alone, neither reducible to the other. The Proxy Knowledge Proof derives that proxy cannot achieve legitimate authority under any framework requiring a basis for authority. Correlation is not access. Prediction is not understanding. No third basis exists. The operation that integrates them, Recognizing, is conjecture. The proofs establish what the operation must achieve. Whether any specific instantiation achieves it under institutional pressure, cognitive bias, and power asymmetry remains empirical. The Rhythm Intelligence Foundation holds the standard and supports the field in finding what works. The stakes are not academic. When the classification is missing and proxy governs without constraint, the consequences are documented and irreversible. Lives ended. Futures made invisible. Decades of warning dismissed. The harm was real. The category was absent. The fog was never confusion. It was category absence producing the only rational response available. The categories have arrived. The fog clears.
Wang Yishun, Wenkai Li, Xiaoqi Li, Zongwei Li · 6 authors
Smart contracts are self-executing programs that manage financial transactions on blockchain networks. Developers commonly rely on third-party code libraries to improve both efficiency and security. However, improper use of these libraries can introduce hidden vulnerabilities that are difficult to detect, leading to significant financial losses. Existing automated tools struggle to identify such misuse because it often requires understanding the developer's intent rather than simply scanning for known code patterns. This paper presents LibScan, an automated detection framework that combines large language model (LLM)-based semantic reasoning with rule-based code analysis, identifying eight distinct categories of library misuse in smart contracts. To improve detection reliability, the framework incorporates an iterative self-correction mechanism that refines its analysis across multiple rounds, alongside a structured knowledge base derived from large-scale empirical studies of real-world misuse cases. Experiments conducted on 662 real-world smart contracts demonstrate that LibScan achieves an overall detection accuracy of 85.15\%, outperforming existing tools by a margin of over 16 percentage points. Ablation experiments further confirm that combining both analysis approaches yields substantially better results than either method used independently.
This paper examines the transformations in finances in the developing markets such as India due to digital money, decentralized finance (DeFi), and blockchain technology. It pays attention to what makes people desire to access such services, what dangers they believe they pose, and how prepared the governmental regulation is (Davis, 1989; Schueffel, 2016). The researchers completed the survey which questioned 420 Indian retail shoppers and fiscal experts about digital finance. They then analyzed the data using Partial Least Squares Structural Equation Modelling (PLS -SEM). They discovered that individuals tend to move to such services when they believe that it is useful, easy to utilize, reputable and with adequate regulation. People fear to take risks and will be less willing to use them (Venkatesh & Davis, 2000; Zhang et al., 2022). The actual use can also be predicted by the intention to use, and individuals who are knowledgeable of contemporary trends, including DeFi, tokenisation, and central bank digital currencies (CBDCs), are even more eager to use useful services (Rogers, 2003; Auer et al., 2022). These findings provide practical suggestions to regulators and banks interested in promoting sound innovation and broader adoption of digital and blockchain finance in India.
Highlights • Identifying threats that cannot be countered by theoretical security based on STRIDE threat analysis of an existing provable data possession (PDP) system • Designing a PDP system with practical security features to counter threats that cannot be addressed with theoretical security alone • Presenting the implementation of the proposed PDP system, PRISM, which is also provided as an open-source software • Validating security properties through property-based fuzz testing with 10,000 randomized test runs per security property • Demonstrating PRISM’s key strengths through comprehensive experiments, including basic performance, trade-offs between processing time and data auditing efficiency, and capabilities for detecting data anomalies Digital platforms are increasingly recognized as a cornerstone for advanced virtual spaces such as smart cities and the metaverse, where vast amounts of data are aggregated, analyzed, and utilized to make critical decisions. These platforms rely on data fusion to integrate diverse sources of information, encompassing individual behavior, urban dynamics, and system states. Through auditing against data tampering, loss, and substitution, enabling the detection of such threats is critical to building a highly reliable system. This paper introduces PRISM (Provable and Immutable Storage Mechanism), an Ethereum-based Provable Data Possession (PDP) system designed to integrate data reliability and security with decentralized auditing. PDP, a cryptographic protocol that enables data integrity in untrusted cloud storage, has seen extensive research focusing on theoretical security and computational efficiency. PRISM extends this foundation by addressing practical security concerns, including the integration of authentication and authorization, data immutability, data uniqueness, data freshness, and state management, to ensure a robust system implementation. Experiments on processing costs and parameter analysis reveal a trade-off between the costs and detection accuracy and demonstrate that PRISM provides efficient data auditing.
With the increasing demand for fair trading in the energy sector, the use of blockchain technology as a new model for energy trading has begun to come into the public eye. However, there are problems such as the lack of a third party to endorse, relatively low efficiency, and unreasonable distribution. The innovative integration of the dynamic proof of stake (PoS) mechanism and entropy regulation strategy in the energy trading sharding system was proposed to solve problems such as trust deficiency, low transaction efficiency and uneven energy distribution existing in traditional energy trading. In the optimized triangular model, verifiable random functions were adopted to select validators, and the staking weights were dynamically adjusted in combination with the real-time status of nodes and transaction activity. Quantify the distribution of equity using Shannon entropy and set a threshold to trigger redistribution. The Pareto frontier solution set for the three objectives of throughput, security and decentralization was solved through the NSGA. A mechanism was adopted to dynamically adjust the equity weight based on the real-time status of nodes and the activity level of energy transactions, effectively enhancing the efficiency and fairness of transaction verification. The regulation was introduced for quantification to reduce the uncertainty and chaos of the energy trading system, ensuring the rational allocation and efficient utilization of energy resources. The experimental results show that optimized system throughput and attack cost have been significantly improved. Meanwhile, the degree of decentralization has risen to 89%, and the overall performance has increased by 12.8 times. This scheme has advantages such as good transaction efficiency, security and energy conservation and consumption reduction, which is conducive to reducing energy transaction costs, enhancing the transparency and stability of the energy transaction market.
Sadly Syamsuddin, Salama Manjang, Muhammad Bachtiar Nappu, Ady Wahyudi Paundu
The increasing demand for sustainable and decentralized energy systems has driven the adoption of blockchain technology in microgrid networks. However, conventional consensus mechanisms, such as proof of work (PoW) and proof of stake (PoS), suffer from high energy consumption, limited adaptability, and fairness issues, which hinder their suitability for dynamic microgrid environments. This paper proposes a novel consensus mechanism, proof of dynamic influence and participation activity (PoDIPA), which integrates prosumers’ real-time participation activity and historical influence into the validator selection process. The proposed mechanism is evaluated through deterministic simulations and compared with PoW and PoS in terms of energy efficiency, transaction processing time, and security resilience. Simulation results demonstrate that PoDIPA significantly reduces average energy consumption and adapts more rapidly to network dynamics while maintaining security performance comparable to existing consensus mechanisms under majority attack scenarios. Although PoDIPA exhibits higher short-term variability due to its adaptive nature, the overall efficiency–stability trade-off remains favorable. These results indicate that PoDIPA is a promising consensus solution for supporting fair, energy-efficient, and decentralized energy trading in future microgrid systems.
إن التقدم التقني المعاصر، وما أحدثه من تقدمٍ في شتى المجالات غير خافٍ على أحد حيث تبع هذا التقدم الكثير من الابتكارات التقنية التي انتشرت بسرعةٍ رهيبةٍ، ومن هذه الابتكارات ما يُعْرف بالرموز الرقمية غير القابلة للاستبدال التي هي عبارة عن أصولٍ رقميةٍ تستخدم لإثبات الملكية الخاصة بالمحتويات الرقمية كمقاطع الفيديو والتغريدات والصور وغيرها، وتمتاز هذه الرموز بكونها غير قابلةٍ للاستبدال، فلا يمكن استبدال رمزٍ منها بآخر نظرًا لندرتها، كما تتمتع بالشفافية والأمان؛ لاعتمادها على تقنية البلوك تشين مما يمنحها قدرًا كبيرًا من الثقة، وقد تناولتُ في هذه البحث التعريف بالرموز الرقمية غير القابلة للاستبدال، وخصائصها، ومجالات استخدامها، ثم تحدثت عن التكييف الفقهي لهذه الرموز، من حيث كونها حقاً معنوياً، والأثر المترتب على هذا التكييف من حيث اعتبارها مالاً، أو عدم اعتبارها مالاً، ومن ثم حكم بيعها بصفةٍ عامةٍ، وبيعها إذا كان البيع بغرض استعمالها استعمالاً محرمًا، أو تكييفها باعتبارها وثيقةً لإثبات الملكية الرقمية، والأثر المترتب على هذا التكييف، ثم ذيلت البحث بخاتمةٍ بينتُ أهم النتائج والتوصيات التي توصلت إليها. The contemporary technological advancements and their impact on various fields are undeniable. This progress has been accompanied by numerous technological innovations that have spread rapidly. Among these innovations are non-fungible digital tokens, which are digital assets used to prove ownership of digital content such as videos, tweets, images, and more. These tokens are characterized by their non-fungibility; one token cannot be replaced by another due to their scarcity. They also possess transparency and security. Because it relies on blockchain technology, which grants it a high degree of trust, this research addresses the definition of non-fungible digital tokens, their characteristics, and their applications. It then discusses the jurisprudential classification of these tokens, considering them as intangible rights and the implications of this classification in terms of whether they are considered property or not. Consequently, it examines the ruling on their sale in general, and specifically the ruling on selling them for the purpose of using them in a prohibited manner. It also addresses their classification as a document for proving digital ownership and the implications of this classification. Finally, the research concludes with a summary of the most important findings and recommendations.
Cloud storage systems have become an integral part of modern data management by enabling users to store and access data remotely.However, traditional cloud storage architectures rely on centralized servers, which introduce several critical challenges such as single-point failure, redundant data storage, increased storage costs, and security vulnerabilities.In earlier systems, data was stored in centralized data centers where duplicate files were repeatedly maintained, leading to inefficient utilization of storage resources.Although basic deduplication methods were implemented, they often compromised data confidentiality and lacked transparency in metadata management.Furthermore, failure of the central server could result in permanent data loss.To overcome these issues, this research proposes the Blockchain-enabled Heuristic Optimized Deduplication Model (BHODM), which integrates blockchain technology, InterPlanetary File System (IPFS), Convergent Encryption (CE), and heuristic-based chunking techniques.In this model, files are divided into optimized chunks based on file size using a heuristic approach.Each chunk is encrypted using CE, where the encryption key is derived from the hash of the data itself, allowing secure deduplication without exposing plaintext data.Duplicate chunks are identified through hash comparison, ensuring that only unique data is stored.The encrypted chunks are stored in IPFS, a decentralized peer-to-peer storage network, while metadata such as file names, block numbers, and hash values are securely maintained in an Ethereum blockchain smart contract, ensuring immutability and transparency.The system is implemented using Django, Web3, IPFS API, and AES-CTR encryption.Experimental results based on storage utilization and computation time demonstrate improved efficiency over traditional approaches.
Dr.B.Swathi Dr.B.Swathi, SAANIYA ARSHI, ARABOTHU ANVESH, MOHAMMED AYAAN AHMED · 5 authors
The quick adoption of blockchain technology and generative AI is a major factor in the world's electricity use, which raises concerns about their long-term environmental impact. To save energy, the first thing you need to do is figure out how much energy you are already using. But because blockchain and generative AI are both cloud-based services, it's not easy to understand how much energy they use when they're not at your site. This makes it harder for companies and organisations that want to improve the accuracy of calculating Scope 3 emissions. This study determines the energy consumption of these technologies at both the system level and per-use basis, comparing them to traditional services such as payment networks and web search engines. For instance, Bitcoin, which uses a Proof of Work (PoW) blockchain, uses about 121 TWh, or 0.43% of all the electricity used in the world. It also uses 720,000 times more energy per transaction than the Visa payment system. When Ethereum switched to Proof of Stake (PoS) in 2022, it used 99.988% less energy, showing how much more efficient things can be.Generative AI models also use a lot of energy, especially when they are being trained and used to make predictions. For instance, it took about 9,450 MWh of energy to train GPT-4, and it took more than 500 MWh of energy to do inference work every day. Inference, which is always powered by user activity, is often more resource-intensive than the training process. The authors say that we need to learn more about and lessen the environmental effects of these technologies right away. Possible solutions include energy-efficient consensus mechanisms or giving AIs the ability to better optimise their own lifecycle. The report is meant to help businesses think about how to use technology in a way that is good for the environment as part of a better or more complete Scope 3 emissions strategy.