This paper focuses on the application of blockchain technology in the field of supply chain finance, with an emphasis on its supportive role in alleviating the financing difficulties of small and medium-sized enterprises. Through theoretical analysis and case study methods, it systematically elaborates how blockchain technology, leveraging its characteristics such as decentralization, traceability, and immutability, enhances the transparency and credibility of supply chain finance, reduces the risks associated with information asymmetry, and thereby improves the availability and efficiency of financing for small and medium-sized enterprises. Taking "Ant Duo-Chain" as an example, the paper analyzes the application effects of blockchain technology in the financing of small and medium-sized enterprises, concluding that this model not only enhances the efficiency of capital circulation but also provides a sustainable path for the stable development and value enhancement of the overall supply chain ecosystem.
Internet of Vehicles (IoV) and IoT environment require decentralized platforms that can support a high number of transactions and provide high security and privacy assurance. This study suggests a reputation-aware, zero-knowledge proof (ZKP) based, dynamically sharded smart contract system that is able to process scalable and privacy-preserving transactions. The suggested architecture highly incorporates dynamic sharding, ZKP-based verification, decentralized smart contracts and reputation-based selection of leaders to jump over the scalability, trusting and performance limitations of traditional blockchain systems. There are also extensive experimental assessments that occur within 100-1000 transactions per second (tps) and batch sizes of 10, 30, 50, and 100. Findings indicate that the given framework demonstrates the ability to scale throughput linearly to about 1000 tps, and Enhanced Fabric and Ethereum reach throughput saturation at 140-150 and 15-20 tps, respectively. The proposed system has an average latency of less than 500 ms at an arrival rate of 1000 tps whereas at the same rate, baseline approaches have a latency of over 8000 ms with larger batch sizes. The success rate of the transaction is always above 97, which is due to the isolation of reputation and adaptive scheduling of shards. Moreover, the framework decreases 40-50% and 45-50% the computation overhead and the cost of communication respectively, over heavyweight baseline schemes. These results show that the synergistic implementation of ZKP, dynamic sharding, decentralized smart contracts, and reputation-aware control are a scalable solution with high throughput IoT and IoV applications that is efficient and secure.
This paper examines cryptocurrency adoption among unbanked, underbanked, and fully banked households in the United States, using data from the 2023 FDIC National Survey of Unbanked and Underbanked Households; the first wave of the survey to include household-level information on cryptocurrency usage. We estimate a Probit model, supplemented by Logit and Linear Probability Model (LPM) specifications as robustness checks, to assess whether underbanked and unbanked households are more likely to adopt cryptocurrency than fully banked households, controlling for a range of demographic and socioeconomic factors. The results consistently show a statistically significant and positive association between underbanked status and the likelihood of cryptocurrency use across all model specifications. Specifically, underbanked households are 1.9 to 2.1 percentage points more likely to use cryptocurrency than their fully banked counterparts, suggesting that cryptocurrency functions as an alternative financial tool for the partially excluded. In contrast, unbanked households either show no statistically significant difference or exhibit a small negative association with cryptocurrency adoption, indicating that cryptocurrency is neither a substitute for formal financial services among the completely excluded nor widely adopted by the fully included. This suggests that those with full access to the financial system likely do not feel the need to seek alternatives. Cryptocurrency adoption is also shaped by key demographic and socioeconomic factors. Younger individuals, men, White respondents, those identifying with two or more races, and individuals with higher income and education levels are significantly more likely to adopt cryptocurrency. Overall, the findings highlight the nuanced role of cryptocurrency as a supplemental financial instrument for the underbanked, rather than a comprehensive solution to financial exclusion particularly for the unbanked.
Cloud-based SaaS platforms now run essential services across finance, healthcare, and government sectors. Many of these systems include automated agents and decision engines that operate at high speed and scale. Identity and access governance therefore serves as a central control layer. Traditional IAM models depend on fixed roles, centralized authorization servers, and periodic reviews. Such structures struggle in distributed, multi-tenant environments that process millions of access requests each day. Prior studies address adaptive authentication, Zero Trust security, decentralized identity, anomaly detection, and cloud resilience. However, these solutions often function separately rather than within a unified framework. This paper introduces a Resilient Identity and Access Governance Architecture that integrates real time risk evaluation, distributed policy enforcement, lifecycle governance for human and machine identities, and fault tolerance in a single design. The framework defines measurable targets for availability, detection time, throughput, and policy propagation. Risk scoring occurs during live authorization decisions, and enforcement spans multiple nodes. The result is a scalable identity governance model suitable for complex SaaS ecosystems that require high availability and consistent control.
The digital art blistering phenomenon has transformed the manner in which art is produced and contextualized in the art markets in the world, though it has occasioned concerns that border issues of ownership, authenticity and provenance which are horrifyingly questioned. Easy reproduction, editing and re transmission, and proving the authorship and protecting of the artists is difficult in digital art. The provided paper explores the possibility of the blockchain technology being a secure and open method of controlling the ownership of the digital art object and enhancing the authenticity records. Blockchain helps in the creation of verifiable histories of digital assets that cannot be changed and this is done by allowing decentralized ledger keeping, cryptographic hash and smart contracts to be used to create the records. The article also involves the research of blockchain-based systems in particular with the involvement of Non-Fungible Tokens (NFTs) as the embodiment of a unique digital piece of art. It proposes a theoretical model that entails artist registration, tokenization of artwork, ownership tracking, and verifications to ensure the safety of provenance and authenticity. The implementation plan will outline the process of the implementation of a blockchain-based tool, namely, the decentralized storage and the automation of smart contracts, which will allow facilitating an effective transaction and the payment of royalty. Furthermore, a comparison of the advantages of introducing blockchain-based systems over traditional models of art ownership is described in terms of the security, transparency, and accessibility. The paper does list such problems as scalability, regulatory uncertainty and environmental issues, despite these advantages. Based on the findings, it can be concluded that blockchain technology can change the digital art ecosystems which provide a reliable platform of ownership verification and empowering artists, and also emphasize the need to conduct further research and sustainable development.
We establish an information-theoretic lower bound on the prover overhead of any zero-knowledge proof system that verifies arbitrary neural network inference. We prove a minimum multiplicative overhead of 2x for general circuits, rising to 4x for neural networks with ReLU activations due to activation encoding, weight commitment, and layer dependency costs. We further prove that composing ZK with fully homomorphic encryption produces multiplicative overhead blowup, making ZK+FHE verification impractical beyond approximately 10^4 gates. We survey six contemporary proof systems and show their observed overheads are consistent with our bounds. Our results formalize the intuition that free verification of AI computation is impossible and provide concrete bounds for system designers.
Open access
2 source records
Adversarial Robustness in Machine Learning
Cryptography and Data Security
Physical Unclonable Functions (PUFs) and Hardware Security
Cheri Venkata Sai, Gurijela Pavan, Pittala Abhirameshwar, S. Suma
These come hand in hand with unprecedented levels of complexity in copyrighting and mon- etizing creations. In general, this protects the copyrights under the existing framework, which are cen- tralized, expensive, and beyond the reach of any independent creator. This paper presents an innovative blockchain-based framework for image copyrighting and social crypto monetization by using blockchain technologies such as Ethereum smart contracts and the InterPlanetary File System (IPFS). The proposed framework enables creators to publish digital images, calculate cryptographic proofs of image ownership with the SHA-256 hashing algorithm, store images in IPFS, and record metadata into the blockchain with unchanged timestamps. In addition, the platform supports “Like to Earn”, where public engagement for viewing is translated directly into rewarding creators with cryptocurrencies via smart contracts. The proposed framework adopts Web3 technologies to enable secure signing of all transactions with fraud prevention using the Elliptic Curve Digital Signature Algorithm (ECDSA) technique through MetaMask wallet authentication. Experimental evaluation of the proposed framework confirms that it can remove duplicate uploads, promptly verify image ownership, and enable social monetization of cryptocurrencies in a secured way.
Open access
2 source records
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Type of the article: Research ArticleAbstractCryptocurrency markets are highly volatile, making price prediction a complex yet essential task for investors, financial engineers, and institutions. The purpose of this study is to evaluate whether Bayesian optimization of technical indicator parameters significantly improves the forecasting performance of Long Short-Term Memory (LSTM) models compared to baseline configurations. The study used daily Bitcoin and Ethereum price data from January 2016 to September 2025. Six technical indicators representing trend, momentum, volatility, and volume-based technical indicators are constructed and dynamically optimized through Bayesian optimization. The optimized indicators are then used as inputs to an LSTM forecasting framework. The study found that the baseline LSTM model achieved moderate predictive accuracy, where Ethereum outperformed Bitcoin. After optimization, both models exhibited improved performance, reducing the forecasting error for Bitcoin by 36.4% and for Ethereum by 12.2%. LSTM model with Bayesian optimized indicators showed a higher forecasting accuracy as compared to the baseline model, with 32% and 18.6% improvements for Bitcoin and Ethereum, respectively. These findings suggest that combining optimized technical indicators with LSTM models enhances predictive power in cryptocurrency markets. The approach offers a robust forecasting framework for traders, analysts, and algorithmic systems in high-volatility environments.Acknowledgment“This work was funded by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [Project No. KFU261690].”
The rapid aging of the global population necessitates automated healthcare environments, yet current Intelligent Room architectures relying on centralized cloud servers face critical challenges regarding data opacity and single points of failure. This paper proposes a novel architecture that synergizes Distributed Ledger Technology (DLT) with Federated Learning (FL) to create a trustless, immutable audit trail for patient monitoring. Unlike traditional FL approaches, we introduce a blockchain-based aggregation mechanism that eliminates the central authority. Furthermore, to address the resource constraints of edge devices such as smartphones, we implement a specific Lightweight Neural Network (L-CNN) utilizing depthwise separable convolutions. The proposed system ensures that patient data remains local while model updates are cryptographically verified on-chain, offering a scalable, low-cost solution for resource-constrained healthcare environments.
This paper discusses the economic principles and policy requirements of cryptocurrency regulation in a more complex and fast developing digital financial ecosystem. It examines how decentralized blockchain-based assets, though having immense advantages such as financial inclusion, efficiency, and innovation, can also create enormous regulatory issues such as market volatility, information asymmetry, illegal financial transactions, and systemic risk. Basing the analysis on the concepts of economic analysis, the authors assess regulation as a method that addresses market failures, distributes resources efficiently, and maximizes social welfare in general. The paper expounds the relevance of cost-benefit modeling in regulatory design, with the emphasis on the fact that policy-makers need to strike a balance between the cost of compliance, administrative burden, and possible limitations on innovation and the benefits of regulatory transparency, investor protection, and financial stability. It also examines incentive systems in international cryptocurrency markets, and especially the so-called regulatory arbitrage, where national regulatory differences affect the geographical location of digital asset practices. One of the priorities is the trade-off between financial privacy and regulatory enforcement. Even though privacy-related aspects of cryptocurrencies can secure the autonomy of users and the safety of their data, they also make it more challenging to trace and intercept illegal financial activities. This article proposes the combination of risk-based and technology-neutral regulatory frameworks that will be capable of adapting to a variety of blockchain applications, such as decentralized finance (DeFi), stablecoins, and tokenized assets. Also, the paper highlights the increasing significance of cross-border coordination and new regulatory technologies (RegTech) to handle cross-border problems and increase the efficiency of compliance. It concludes that to have good cryptocurrency governance, there must be an interdisciplinary approach that comprises of economic theory, legal analysis, and understanding of technology. This balancing and adaptive action is what is needed to encourage innovation and still maintain financial stability, investor protection, and overall interest of the digital economy by the general population.
This paper examines the evolving relationship between blockchain architecture and financial privacy, focusing on the inherent tension between transparency, pseudonymity, and regulatory oversight. It begins by analysing the structural foundations of blockchain systems, including distributed ledgers, cryptographic security, and decentralized consensus mechanisms, which collectively replace institution-based trust with system-based verification. While such architecture enhances transparency and immutability, it simultaneously generates new privacy challenges. Through a comparative analysis of Bitcoin, Monero, and Zcash, the paper highlights a spectrum of privacy designs within the cryptocurrency ecosystem. Bitcoin represents a model of transparent yet pseudonymous transactions, where public ledger visibility enables traceability despite the absence of explicit identity markers. In contrast, Monero adopts a privacy-centric approach using ring signatures, stealth addresses, and confidential transactions to obscure sender, receiver, and transaction value. Zcash introduces a hybrid model, employing zero-knowledge proofs (zk-SNARKs) to reconcile transactional confidentiality with verifiability, alongside selective disclosure mechanisms. The study further explores the limitations of transparent blockchains, including risks of transaction traceability, address clustering, and linkage to real-world identities through regulatory touchpoints such as exchanges. It also evaluates the regulatory implications of privacy-enhancing technologies, particularly their impact on anti-money laundering (AML) and counter-terrorism financing (CTF) frameworks. The paper underscores the growing role of international standards and regulatory bodies in shaping compliance mechanisms within decentralized ecosystems. Finally, the paper considers emerging solutions such as privacy-preserving smart contracts, decentralized identity systems, hybrid blockchain models, and regulatory technologies (RegTech), which aim to balance user privacy with legal accountability. It argues that the future of blockchain governance lies not in choosing between transparency and privacy, but in developing adaptive frameworks that integrate both. The study concludes that achieving this balance will require sustained interdisciplinary collaboration and coordinated global regulatory efforts.
As a core technology of blockchain ecosystems, smart contracts are fundamentally reshaping the operational logic and commercial landscape of the digital economy. This paper systematically analyzes the current economic ecosystem of smart contracts from four dimensions: business value creation, multi-dimensional industry applications, legal and regulatory challenges, and frontier technological evolution. Research indicates that smart contracts, by reducing transaction costs and enhancing trust mechanisms, have achieved maturity in financial applications (DeFi) and are demonstrating significant enabling effects in real-economy sectors such as supply chains, healthcare, and energy internet. However, their widespread implementation still faces institutional obstacles including ambiguous jurisdictional boundaries, compatibility between code and law, and liability attribution. Looking ahead, Layer-2 scaling solutions have significantly improved cost-effectiveness, while the integration of cross-chain interoperability and AI-driven intelligent decision-making will become key trends driving the expansion of "contractability" boundaries. Smart contracts are not merely technical tools but rather institutional infrastructure driving the transformation of business models from intermediary-dominated to algorithm-autonomous paradigms.
P. Subramanya Sai, Bhukya Niranjan, Akula Tejaswini, A Varshitha · 5 authors
The exponential increase in digital data exchange and online communication has intensified the need for secure, transparent, and dependable file-sharing systems. Critical information such as financial records, healthcare data, confidential documents, and research outputs is frequently transmitted across distributed networks, where conventional centralized storage models introduce significant vulnerabilities. These traditional systems rely on single-point control, making them prone to data breaches, unauthorized access, service disruptions, and integrity violations. Furthermore, they lack transparency and robust audit mechanisms, raising concerns about data reliability and trustworthiness during storage and transmission. To overcome these limitations, this work proposes a decentralized and secure file-sharing framework that integrates blockchain technology, the Inter-Planetary File System (IPFS), and cryptographic techniques. The system ensures data confidentiality by encrypting files using the Elliptic Curve Integrated Encryption Scheme (ECIES) before storing them in the distributed IPFS network. Instead of placing the actual data on-chain, only essential metadata including file hash, ownership details, timestamps, and access permissions is maintained within a smart contract on the Ethereum blockchain. This design guarantees immutability, traceability, and protection against tampering, while enabling fine-grained access control. In addition to secure storage, the framework incorporates ChaCha20-based symmetric encryption to evaluate and compare computational performance with asymmetric methods. The combination of decentralized storage, cryptographic security, and immutable ledger technology eliminates reliance on centralized authorities, thereby reducing single points of failure and enhancing system resilience. The proposed approach ensures that only authorized entities can access and decrypt shared content while maintaining transparency of file transactions.
This paper examines the complex and evolving relationship between blockchain technology and the General Data Protection Regulation (GDPR), focusing on the fundamental tension between blockchain’s immutability and the data protection rights of individuals. While blockchain offers transformative advantages such as decentralization, transparency, security, and trustless verification, its core architectural feature—immutability—poses significant challenges to compliance with key GDPR principles, particularly the right to erasure, rectification, and data minimization. The study critically analyzes how decentralized and borderless blockchain networks disrupt traditional legal frameworks that rely on identifiable data controllers and territorially bounded regulation. It explores the difficulties in assigning legal responsibility within distributed systems, as well as the complications arising from cross-border data transfers and jurisdictional ambiguities. Further, the paper evaluates emerging technical and regulatory responses aimed at reconciling these conflicts, including off-chain data storage models, encryption-based deletion (crypto-shredding), pseudonymization, and advanced privacy-preserving techniques such as zero-knowledge proofs. It also considers the role of privacy-by-design principles and the need for adaptive regulatory frameworks tailored to decentralized technologies. The paper concludes that the interaction between blockchain and GDPR represents a broader challenge in contemporary law: balancing technological innovation with the protection of fundamental rights. It argues for a coordinated, interdisciplinary approach involving legal scholars, policymakers, and technologists to develop flexible and forward-looking governance models capable of addressing the unique characteristics of decentralized digital ecosystems.
With the rapid iteration of blockchain technology, smart contracts, as core components of decentralized applications, directly impact the stability of on-chain assets and ecosystems through their security. Traditional vulnerability detection methods primarily rely on expert rules and static analysis, facing bottlenecks such as high false positive rates and poor adaptability to complex logical vulnerabilities. In recent years, Large Language Models (LLMs), with their exceptional code understanding and reasoning capabilities, have provided new technical pathways for smart contract security auditing. This paper focuses on LLM-driven smart contract vulnerability detection technologies, systematically reviewing mainstream application paradigms from prompt engineering to model fine-tuning. The paper first reviews the current state of smart contract security and the limitations of traditional methods; subsequently, it provides in-depth analysis of the architectural design and core mechanisms of representative frameworks such as GPTLens and SmartVD, evaluating their performance in detection accuracy and recall rate; finally, addressing current challenges including data scarcity, model hallucinations, and computational overhead, it proposes future evolution directions such as multimodal fusion and human-in-the-loop auditing, providing reference for research and practice in related fields.
The modern educational prison has no bars. Students attend, comply, pass tests, receive diplomas — and emerge unable to use what those diplomas certify. This paper formalizes that decoupling as CM-001 (Idolatry Failure) in educational governance using the Zero Leap Theory (ZLT) framework. When the Alignment Gate (Φ) closes — through mandatory curricula, high Forced Exposure Ratios (FER), and assessment-driven instruction — the system enters an Idolatry Failure regime: the proxy (standardized scores, compliance metrics) is optimized while the structural target (transferable learning capacity, V_real) drifts. The Structural Delusion Index for Education (SDI_edu) is positive and growing. Internal metrics report success. External audit reveals divergence. This paper is the structural complement of ZLT-P15 (The Credential Standardization Trap). P15 addresses the quadrant where exit is blocked (C → 0): coercion is visible. P16 addresses the orthogonal quadrant: exit is available (C > 0), but Φ has closed. Students remain. Metrics stay green. The structural damage is invisible — precisely because assessment pressure simultaneously suppresses the behavioral signals (dropout, resistance) that would trigger correction. This is the Invisible Prison: a five-criterion observable configuration detectable with existing OECD instruments. Core empirical result: Using PISA 2022 and the OECD Survey of Adult Skills 2023 across 33 OECD countries: ρ(FER, SDI_edu) = 0.795, p < 0.0001. Higher Forced Exposure Ratio is strongly associated with greater proxy/competence decoupling. t-test high-FER vs low-FER countries: t = 4.986, p = 0.0008. Prediction 3 of ZLT-P16 confirmed. The structural triangle: South Korea presents the canonical case of sophisticated proxy/competence decoupling: top-3 PISA globally, 79% school belonging (above OECD average), but 22% life dissatisfaction (above OECD average). In the Survey of Adult Skills 2023, Korean adults (16-65) score below the OECD average in numeracy and adaptive problem-solving. Among Korean youth (16-24), literacy is above OECD average, but adaptive problem-solving falls below — the structural signature of compliance-type learning: succeeds where assessment trains, fails specifically on transfer competence. Finland is the proof-of-concept: comparable proxy outcomes, SDI_edu near zero, adult skills above OECD average across all three domains including youth 16-24. Two core corollaries derived from CM-001/P75 and I-3/P_SYNTH: P16.C1 (Alignment-Divergence): when Φ < Φ_min, SDI_edu grows monotonically — proxy improves while ρ(proxy, V_real) declines. P16.C2 (Invisible Acquiescence): the conditions that close Φ simultaneously suppress the behavioral signals that would alert the system to its own failure. The prison self-conceals. New constructs: SDI_edu three-layer audit family (SDI-1 within-system, SDI-2 young-adult orthogonal, SDI-3 long-run structural); Compliance Cascade Onset Point (CCOP); Invisible Prison as five-criterion observable state; IAS-CUR v2.1 audit standard; Attribution Inversion Principle (P16.AIP). This Zenodo record includes: ZLT_P16_v2_2_zenodo.pdf — full paper (14 pages, DOI embedded) ZLT_P16_v2_2.docx — Word source ZLT_P16_v2_1_with_appendices.docx — version with embedded figures and Appendices D–F zlt_cur_audit.py — IAS-CUR v2.1 executable audit tool (4/4 canonical tests passing; run: python zlt_cur_audit.py --test) zlt_p16_phase_diagram.py — Phase Diagram Suite: FER vs SDI_edu for 33 OECD countries (run: python zlt_p16_phase_diagram.py) zlt_p16_knowledge_decay.py — Knowledge Decay Index (KDI) exploratory tool with honest null result zlt_p16_ias_cur_interactive.html — IAS-CUR v2.1 interactive browser-based audit (open in any browser, no server needed) 9 companion PNG figures (phase diagram suite + KDI suite) All scripts run in Google Colab without API keys or external downloads. No authentication required. Part of the Zero Leap Theory (ZLT) series. Upstream: ZLT-P75 (CM-001 / doi:10.5281/zenodo.18940742), ZLT-P_SYNTH (I-3, OD-1 / doi:10.5281/zenodo.19498184), ZLT-P15 (structural complement / doi:10.5281/zenodo.18344309). Series root: doi:10.5281/zenodo.18264279. v2.2 changelog: Corrects empirical overclaim on Korea 16-24 youth data present in v2.1. Accurate data per OECD Survey of Adult Skills 2023: literacy above OECD average, numeracy near average, adaptive problem-solving below OECD average. Domain-specific decoupling framing (compliance-type learning fails specifically on transfer competence) replaces the v2.1 maximal claim. Theory, structure, and all other empirical claims unchanged. Companion tools added in this version.
The quick proliferation of cryptocurrency markets has essentially transformed the financial frameworks of the globe in that it has made it possible to initiate the means of value transfer across borders that are decentralized, borderless, and technologically advanced. Cryptocurrencies are based on blockchain and cryptographic protocols and enable peer-to-peer transactions without use of traditional financial intermediaries, which improves efficiency, lowers the costs of transactions, and increases financial inclusion, especially in underserved areas. In addition to payments, the technologies have stimulated innovation in fields like decentralised finance, smart contracts, and systems of digital identity. But the very same characteristics which render cryptocurrencies appealing also pose serious threats to regulation and law enforcement. The anonymity of transactions combined with the decentralized and cross-border structure of blockchain networks make it difficult to determine who the users are and apply jurisdiction-specific legislation. As a result, cryptocurrencies have become more and more related to different types of financial crime such as money laundering, terrorist financing, tax evasion, ransomware attacks, and illegal trading in darknet markets. The paper discusses the principal types of crime in the context of cryptocurrency and evaluates the challenges encountered by regulatory bodies and law enforcement agencies that might need to overcome these challenges. It also discusses the international regulation reaction, including the involvement of the international standard-setting organizations and the development of compliance systems, including anti-money laundering (AML) and know-your-customer (KYC) systems. Furthermore, the paper also mentions that technological solutions, such as blockchain analytics, are increasingly gaining significance in enhancing investigative potential. The paper concludes that, regardless of the revolutionary potential of cryptocurrencies in terms of financial innovation, their productive regulation involves a moderate and coordinated strategy, incorporating legal and regulatory models, technological progress, and global collaboration.
We present the Creation Ledger, a protocol for token economics grounded in verified creative process rather than computational expenditure, staked capital, or attention signals. The protocol introduces Proof of Provenance (PoP) — a consensus primitive where tokens are minted exclusively through verified human-directed creative work, as validated by Cognitive Rhythm Analysis (CRA) of embedded decision trees. The protocol addresses three structural failures that have destroyed every prior creator token platform: (1) speculation crowding out utility at launch, (2) verification systems that are cheaper to forge than to use honestly, and (3) regulatory classification as securities due to revenue-sharing mechanics. The system is protected by nine provisional patent applications (193 claims across four patent families) filed with the United States Patent and Trademark Office. Patent Support: Patents 1-9 filed March 30, 2026. 193 total claims across Dense Notation, Proxy Provenance, Self-Proving Documents, Decision-Chain Provenance, Schema Registry, Delivery Protocol, Code Closure, Format-Provenance Fusion, and Document-Directed Computation.
Peer-to-peer (P2P) payments facilitate rapid direct transactions but are frequently compromised by trust asymmetry, leading to substantial risks of non-delivery or non-payment. This study addresses these vulnerabilities by introducing a lightweight, deterministic escrow mechanism based on Ethereum smart contracts, specifically designed to bridge the regulatory gap in consumer protection. Unlike conventional escrow systems that rely on costly human intermediaries or complex decentralized autonomous organization (DAO) structures, the proposed "FairPay" model advances the state-of-the-art by offering a streamlined five-state lifecycle architecture comprising Created, Funded, WorkSubmitted, Released, and Refunded stages. The research prioritizes an analytical problem-solution flow, focusing on a state-machine design that enforces automated role-based restrictions. Methodological evaluation conducted on the Ethereum Sepolia testnet demonstrates a 100% functional success rate across all unit test scenarios. Furthermore, gas cost analysis reveals that the system is economically viable for granular transactions, with core operational functions maintaining a low execution overhead. Beyond operational success, the primary scholarly contribution lies in the design insight of balancing high cryptographic security with granular transaction accessibility, providing a scalable framework for the modern digital economy. However, the system currently assumes binary participant decisions for work verification, representing a transparency-oriented limitation in handling highly subjective service deliverables. Ultimately, this study demonstrates that algorithmic trust, mediated through a simplified state-machine, offers a more efficient and transparent alternative to existing high-complexity blockchain models, effectively resolving the tension between decentralized security and practical usability in P2P digital interactions.
Legal certainty is a fundamental prerequisite for implementing decentralized governance, particularly to ensure protection for the apparatus in developing public service innovations. However, regulatory fragmentation often leads to overlapping authority and administrative uncertainty at the local level. This research aims to examine the legal certainty construction of innovation through a vertical synchronization test of Regulation Number 20 of 2021, and to evaluate its sociological implications on the fluctuation of regional innovation performance. This research employs a normative legal method calibrated with policy evaluation through statutory and conceptual approaches, and analyzes Regional Innovation Index data for the 2020 to 2025 period. The analysis results indicate that the Regent Regulation suffered multilayered substantive defects over time. In the initial formulation phase, the budget deprivation threat sanction for innovations deemed unsuccessful contradicted the apparatus protection principle under Law Number 23 of 2014. Furthermore, the local government has been found to have committed legislative omission by failing to amend the Regent Regulation to accommodate the updates to the institutionalization obligation and affirmative financing guarantees mandated following the promulgation of Ministerial Regulation Number 91 of 2021 and Governor Regulation Number 3 of 2022. Factual evidence confirms that this static and defective legal formulation triggered a climate of bureaucratic fear, resulting in a drastic decline in the regional innovation score in the first year of the regulation’s implementation and subsequent stagnation. In conclusion, punitive innovation regulations that are unresponsive to hierarchical updates have degraded the essence of regional autonomy and created a governance paradox. Therefore, the revocation of the administrative sanction clause and the execution of the institutionalization amendment must be executed to restore a safe authorizing environment for experimental legislation.
Rizqi Akbar Makarim, Desinta Maheswari, Aqila Dina Pramustiwi, Kartika Ayu Rahmawati · 5 authors
The volatility of cryptocurrency markets has increased substantially in recent years, particularly for Ethereum (ETH), which exhibits fat-tailed distributions and persistent volatility clustering that traditional linear models are unable to capture. This study aims to analyze and model the volatility of ETH/USD using high-frequency hourly data to determine the most appropriate volatility model for describing Ethereum’s intraday market dynamics. The dataset consists of 8,760 hourly closing prices from October 31, 2024 to October 31, 2025, obtained through the CryptoCompare API. The methodological framework includes data preprocessing, log-return transformation, stationarity analysis using the Augmented Dickey–Fuller test, detection of heteroskedasticity via the ARCH–LM test, and estimation of several ARCH and GARCH model specifications. The results show that ETH/USD returns are stationary, non-normally distributed, and exhibit clear volatility clustering. Among the ARCH models, only ARCH(1) adequately captures short-term fluctuations, while ARCH(2) provides no additional benefit. In contrast, GARCH models demonstrate superior performance in capturing both short-term shocks and long-term persistence. Based on AIC, BIC, and log-likelihood values, GARCH(1,2) emerges as the best-performing model, offering the highest flexibility in representing Ethereum’s persistent and reactive volatility patterns. These findings confirm that ETH/USD volatility is predictable and can be modeled statistically. Future research may incorporate asymmetric GARCH extensions or external explanatory variables to improve predictive performance.
Jorge Garcia-Diaz, Daniel Escánez-Expósito, Pino Caballero-Gil, Jezabel Miriam Molina-Gil
Abstract Zero Knowledge Proofs based on computational hardness assumptions are fundamental primitives for secure user authentication. This paper proposes a novel Designated Verifier Zero Knowledge Proof that leverages the inherent randomness of quantum bits. Unlike traditional constructions relying on computationally intractable NP problems, the proposed protocol derives its security from the physical layer, specifically the uncertainty principle of quantum state projections in misaligned measurement bases. A rigorous formal mathematical analysis establishes the completeness, soundness and zero knowledge properties of the scheme. Furthermore, the protocol’s performance is evaluated via a quantum simulator under realistic error-prone conditions. The results demonstrate that the construction is robust against dishonest parties while remaining feasible under constrained quantum resources, offering a scalable approach for secure quantum authentication.
Decentralized applications (DApps) represent a paradigm shift in software architecture, leveraging blockchain technology and distributed consensus mechanisms to eliminate single points of failure and centralized control. As the adoption of DApps accelerates across sectors such as finance, supply chain, healthcare, and governance, ensuring their functional correctness and behavioral reliability has become a critical engineering challenge. Unlike traditional software, DApps operate in adversarial, permissionless environments where smart contracts execute autonomously and immutably on distributed nodes, making post-deployment correction extremely costly or impossible. This review systematically examines the landscape of functional testing methodologies tailored for decentralized applications, analyzing their suitability, limitations, and practical applicability in modern DApp development workflows. We survey research spanning smart contract verification, consensus protocol testing, oracle interaction validation, cross-chain interoperability testing, and user-layer functional testing of Web3 interfaces. The review identifies four dominant testing paradigms: (1) unit testing of smart contract functions, (2) integration testing of DApp components, (3) property-based testing using formal specifications, and (4) end-to-end simulation on testnets. Through comparative analysis across 13 seminal studies, we evaluate each approach along dimensions of automation feasibility, coverage depth, gas efficiency awareness, and scalability to complex DApp ecosystems. Our findings indicate that while static analysis and symbolic execution tools such as Mythril, Slither, and Manticore offer strong vulnerability detection, they address security properties more than functional correctness. Conversely, framework-based testing tools like Hardhat, Truffle, and Foundry provide adequate unit-level coverage but struggle with cross-contract orchestration and event-driven logic verification. A critical gap exists in testing oracle-dependent and DAO governance workflows. This review concludes with a synthesis of best practices, open research challenges, and a directional roadmap for developing holistic functional testing frameworks suited to the evolving complexity of decentralized systems.