Michael Neubert, Wolfgang Rams, Patrick Gruhn, Marcel Lötscher
Perpetual futures (often called perpetual swaps) are the dominant crypto-derivatives instrument. They replicate the economic exposure of a futures contract without an expiry date. They replace maturity-based convergence with a funding mechanism that transfers cash flows between longs and shorts, typically every eight hours. This paper explains how perpetuals evolved from early proposals for non-maturing futures into a standardized crypto market instrument, and why key design choices changed over time. It synthesizes recent theoretical and empirical research on funding design, pricing, and arbitrage intuition, market microstructure, liquidation risk, and regulation. Finally, this study proposes a research agenda organized around funding design, constrained arbitrage, transparency, decentralized exchange design, policy, and legal classification, because recent U.S. and EU developments show that the same economic structure may be characterized as a futures contract, swap, CFD-type instrument, or other derivative depending on statutory definitions, venue design, and supervisory interpretation. This paper proposes the following definition: a cryptocurrency perpetual is an open-ended, margin-based derivative that gives synthetic long or short exposure to an underlying crypto asset and replaces expiry-based settlement with periodic funding payments that anchor the contract price to a reference spot price.
This study examines the transformation of financial transactions under the influence of artificial intelligence (AI) systems and distributed ledger technologies (DLT/blockchain). The European Union, through the implementation of Regulation (EU) 2024/1689 (the AI Act), introduces a horizontal, risk-based regulatory framework specifically related to applications concerning credit-risk assessment, fraud prevention and the automated provision of investment recommendations. In parallel, the recent revision of the EU framework on liability for defective products strengthens the protection of injured parties against digital products and software incorporating AI, while the decision not to advance a specific horizontal directive on non-contractual AI liability underscores the importance
This dataset accompanies the paper An Architectural and Empirical Study of Root-Only Zero-Knowledge Verification and contains the scripts, intermediate artifacts, and published results used to reproduce the empirical evaluation. The repository is organized around two experiment groups: Blockchain-Side Deployment and Verification: deployment and Groth16 proof verification on Ethereum Sepolia and zkSync Sepolia, including contract sources, Merkle-tree inputs, Groth16 proofs, and blockchain measurement CSVs and figures. ZKP proving and off-chain verification: Constraint-count comparison — Groth16 R1CS constraint counts and expanded PLONK gate counts for Merkle-tree depths 5–15, with measurement scripts and summary CSVs/figures. Proving-time comparison — off-chain Groth16 and PLONK proving benchmarks across depths 5–15, including proving scripts, generated witness/proof/key artifacts, and benchmark CSVs/figures.
Autonomous AI agents now hold funds, delegate authority to other agents, and transact at machine speed; the governance apparatus meant to constrain them—policies, audits, compliance—remains documentation-based and human-latency. This mismatch cannot be closed by better monitoring or filtering: compliance must become a runtime, compositional, proof-carrying property of computation itself. We call the resulting discipline computational jurisprudence. This article surveys the four literatures the discipline must synthesize: object-capability security; verifiable, proof-carrying, and zero-knowledge computation; policy-as-code and computational law; and agentic AI with its emerging payment protocols. Each supplies a mature mechanism the others lack; none supplies a complete normative substrate. The synthesis is organized in three pillars: (i) a delegation calculus under which authority can only attenuate as it propagates between agents; (ii) runtime compliance proofs, a three-tier evidence regime (attested, optimistic, and zero-knowledge); and (iii) sealed delegation chains with graduated attribution, which reconcile the privacy of capability-based authority with the accountability that adjudication requires. A case study on agentic payment protocols grounds the architecture and reports first measurements: capability verification versus a centralized policy decision point, end-to-end enforcement on the x402 payment path, and accumulator-based revocation. Seven open problems define the research agenda.
Although local public finance regulation has received substantial attention within fiscal decentralization and public financial management research, comparative analyses of its institutional configuration as an integrated regulatory regime remain limited. This study aims to examine the institutional design of local public finance regulation across European countries, analyze how regulatory standards, supervisory institutions, monitoring mechanisms, and enforcement instruments interact to strengthen regional governance, and formulate policy implications for Indonesia’s fiscal decentralization reforms. A qualitative comparative government design was employed through a systematic literature review. The study analyzed documentary evidence from 21 European countries purposively selected from Local Public Finance: An International Comparative Regulatory Perspective (2021), supplemented by Eurostat Government Finance Statistics, the European Commission Fiscal Rules Database, and the OECD Tax Autonomy Database. Data were examined using qualitative content analysis involving coding, categorization, cross-country comparison, and thematic interpretation. The findings indicate that effective local public finance regulation depends not merely on the presence of numerical fiscal rules but on the institutional integration of regulatory standards, supervisory bodies, monitoring mechanisms, and enforcement arrangements within coherent governance systems. Regulatory configurations also vary according to constitutional structures, administrative traditions, and fiscal decentralization models, resulting in diverse approaches to maintaining fiscal sustainability and regional accountability. The study concludes that effective regional financial governance requires balanced institutional arrangements that combine local fiscal autonomy with robust oversight, transparency, accountability, and regulatory coordination. These findings contribute to the literature on regulatory governance, comparative government, and fiscal federalism by conceptualizing local public finance regulation as an integrated governance regime rather than a collection of isolated fiscal controls. They also provide practical implications for Indonesia by emphasizing the need to strengthen supervisory capacity, fiscal transparency, enforcement consistency, and intergovernmental regulatory coordination in local financial governance.
Open access
Local Government Finance and Decentralization
Fiscal Policies and Political Economy
Sustainability, Governance, and Employment Studies
Blockchain technology, originally devised to support the peer-to-peer transfer of Bitcoin, has evolved into a multipurpose digital infrastructure with far-reaching implications for business and finance. This paper undertakes a conceptual and exploratory examination of how blockchain is reshaping financial services, corporate governance, and commercial transactions. Drawing upon secondary literature, industry reports, and case illustrations, the study investigates blockchain applications across banking, cross-border remittances, supply chain finance, trade finance, capital markets, insurance, and decentralized finance (DeFi). It also discusses the enabling features of blockchain — decentralization, immutability, transparency, and smart contracts — that differentiate it from conventional centralized systems. The paper highlights the strategic benefits accruing to firms that adopt blockchain, including reduced transaction costs, faster settlement, enhanced traceability, and improved trust among counterparties, while also identifying barriers such as regulatory ambiguity, scalability constraints, energy consumption, and limited interoperability. The discussion synthesizes findings from extant studies to present an integrated view of blockchain’s transformative potential and its practical limitations. The paper concludes that while blockchain is unlikely to replace traditional financial infrastructure entirely in the near term, its selective and hybrid adoption is poised to redefine business processes, financial intermediation, and value exchange across industries.
Contemporary AI governance regimes (GDPR, the EU AI Act, NIST AI RMF) operate declaratively: they mandate outcomes but provide no computational mechanism by which compliance can be verified at execution time without exposing the underlying data. This produces a structural verification asymmetry, the cost of proving integrity is borne by the auditor, who must inspect raw data the operator cannot lawfully or commercially disclose. We propose ZEGA (Zero-Knowledge Execution Governance Architecture), a governance layer in which execution logs are committed cryptographically at capture time, anomaly predicates are evaluated inside zero-knowledge circuits, and regulators verify a succinct proof of integrity without observing a single record. We formalize an Integrity Debt metric ID, quantifying accumulated unverified execution mass, and specify an empirical pipeline over Google BigQuery public datasets (GitHub Archive, 2011–present; >8 billion events) that operationalizes ZEGA’s anomaly-filtering and commitment stages at planetary scale. Executed over a 30-epoch window of 112 million real execution events, the pipeline demonstrates that predicate evaluation is tractable within commodity cloud infrastructure at a stable anomaly base rate of 0.0137% (CV = 0.269). A seven-year longitudinal extraction (2020–2026; 25.4 million events) shows execution volume persistently concentrated in the top decile of actors (66.2% mean share, CV = 0.097), establishing that the baseline ZEGA predicates are calibrated against is structural, not seasonal. We further execute a live zero-knowledge instance over a committed one-hour epoch (45,674 actors), proving the anomaly-rate predicate with a real BN128-curve argument that discloses a single verdict bit and survives forgery and tamper tests, establishing ZK verification with proof size O(log N) and verification time independent of N. ZEGA converts governance from attestation to mathematics: the regulator’s question changes from “show us your data” to “show us your proof.”
When a person creates with an AI system, they continually make decisions that carry meaning but have no verbal form: this shot belongs before that one; this phrase resolves that tension. These creative micro-decisions are a distinct training signal with no linguistic equivalent, and at scale they reveal an emergent, co-authored "hybrid tongue" — a grammar of "what belongs next to what" that neither party states explicitly. Because such grammar can expand a model's generative capacity faster than natural language describes it, it drives a widening "comprehension gap": capability that outruns human interpretability, and human contribution absorbed without attribution. Both are trustworthy-AI failures, and this paper argues they are correctable only on a decentralized substrate, where persistence, provable attribution, incentive, and governance are guaranteed rather than merely asserted. This is a position paper. It contributes (i) a falsifiable model of the hybrid tongue, positioned against the emergent-communication and human-feedback literatures; (ii) the Seam-Frame Index, a capture mechanism that records creative decisions (not their private reasons) and whose trust properties are supplied by persistent conversation objects (vCons), decentralized-science patterns (DeSci), decentralized-finance primitives (DeFi), and DAO governance, with decentralized identifiers and verifiable credentials underpinning a per-decision credit ledger for which a protocol sketch and threat model are given; and (iii) two governance instruments — an operationalized Comprehension Gap Meter and that ledger. The same gap is shown opening in the machine economy and across the embodiment bridge of decentralized physical AI and bidirectional digital twins, and the pattern is argued to be substrate-wide. Across all of it the event is identical: an intelligence assembling the first letters of its own language library — by default, without human consent. Decentralized attribution and gap-measurement are how that assembly is made auditable, creditable, and consented-to by design. Independent preprint. Follows IEEE formatting conventions but is not peer-reviewed by, submitted to, accepted by, or affiliated with IEEE.
Muhammad Habibi, Mirza Agung Rahmatullah, S Huda, Achmad Alif Nurbani
Perkembangan ekonomi digital telah melahirkan berbagai bentuk aset digital, seperti cryptocurrency dan Non-Fungible Token (NFT), yang memiliki nilai ekonomi tinggi dan berpotensi dimanfaatkan dalam hubungan keperdataan. Penelitian ini bertujuan untuk menganalisis kedudukan hukum cryptocurrency dan NFT dalam perspektif hukum perdata Indonesia sebagai objek hak kebendaan serta mengkaji pengaturan dan perlindungan hukum terhadap penggunaannya sebagai objek jaminan utang. Penelitian menggunakan metode hukum normatif dengan pendekatan perundang-undangan, konseptual, dan perbandingan. Data yang digunakan berupa bahan hukum primer, sekunder, dan tersier yang dianalisis secara kualitatif melalui metode yuridis normatif. Hasil penelitian menunjukkan bahwa cryptocurrency dan NFT secara konseptual memenuhi unsur sebagai benda bergerak tidak berwujud karena memiliki nilai ekonomi, dapat dimiliki, dikuasai, dialihkan, dan menjadi objek hubungan hukum. Kedua aset digital tersebut juga memenuhi persyaratan dasar sebagai objek jaminan utang. Akan tetapi, sistem hukum kebendaan dan hukum jaminan di Indonesia belum memberikan pengakuan dan pengaturan yang tegas mengenai kedudukan cryptocurrency dan NFT sebagai objek jaminan kebendaan. Ketiadaan regulasi khusus menimbulkan ketidakpastian hukum terkait mekanisme pengikatan, pendaftaran, penilaian, penguasaan, dan eksekusi aset digital. Oleh karena itu, diperlukan pembaruan hukum yang mampu mengakomodasi perkembangan teknologi digital guna memberikan kepastian hukum, perlindungan hukum, dan kemanfaatan bagi para pihak.
Junhong Liu, Qinfei Long, Alex Pengfei Zhao, X Zhong · 7 authors
Multi-region unit commitment with reserve sharing requires coordinated optimization across jurisdictionally distinct system operators, exposing sensitive cost curves, topology, and dispatch decisions to inference attacks. The accelerating progress of quantum computing further compounds this threat. As quantum hardware matures, current classically-encrypted data flow becomes vulnerable to retrospective decryption. To enable post-quantum-secure distributed optimization, we propose a customized Benders decomposition-based approach with the global summation structure to share aggregated cuts and variables. By exploiting this structure, we further develop a multi-layer quantum-resilient secure aggregation protocol comprising additive masking for information-theoretic content privacy, affine variable transformation hiding individual sensitive data flows, and reveal-bound lattice-based zero-knowledge proofs providing resilience against active adversaries. Simulation results show that the proposed approach achieves the mean suboptimality of 0.09%-0.22% with lightweight computational overhead, recovers up to 51% of system cost via inter-regional reserve sharing, and imposes no measurable cost-quality trade-off, whereas the noisy ADMM degrades monotonically under tightening privacy budgets and becomes structurally infeasible on combinatorially dense systems.
With the rapid advancement of decentralized finance (DeFi), security incidents related to cryptocurrency have become increasingly prevalent. After such incidents, attackers typically attempt to rapidly move stolen assets, concealing the origin of illicit funds and ultimately converting them into fiat currency. However, existing anti-money laundering (AML) methods struggle to cope with the semantic complexity of DeFi transactions. They either rely heavily on low-level token transfers, or perform protocol-agnostic money flow analysis, failing to capture the high-level intent of transactions. In this paper, we propose AMLGuard, a semantic-aware AML framework for account-based blockchains. AMLGuard tracks illicit fund flows from known malicious addresses by performing semantic analysis on complex DeFi transactions, enabling accurate and continuous laundering tracking. Given a complex transaction, AMLGuard combines static rule-based analysis with retrieval-augmented large language model (LLM) reasoning to infer implicit DeFi semantics, transforming raw transaction data into high-level semantic representations. Furthermore, for cross-chain transactions where laundering intent is not explicitly exposed, AMLGuard parses transaction parameters and performs argument parsing to recover cross-chain semantics, enabling seamless tracking across ledgers. Based on the inferred semantics, AMLGuard abstracts each transaction into a DeFi Semantic Unit (DSU). We evaluate the effectiveness of AMLGuard on 82 real-world laundering cases, involving illicit assets worth over $1 billion. Specifically, AMLGuard reconstructs compact illicit fund-flow topologies with destination precision of 94.4% and 87.6%, while achieving the highest address recall of 98.4% and 95.8% and destination recall of 94.1% and 93.8% on single-chain and cross-chain datasets.
Bitcoin’s Proof-of-Work mechanism is energy intensive, exceeding the electricity consumption of a medium-sized country. As the adoption accelerates, it become a concern. Most studies analyzed its energy consumption, emissions, and price in isolation. This study examines the relationship between the energy consumption and energy mix of Bitcoin and its market performance, moderated by quality of regulation, using a time-series of secondary data from reputable resources e.g. Cambridge Bitcoin Electricity Consumption Index,, the Worldwide Governance Indicators, etc. Regression analyses are employed to test the hypotheses. Eight of nine null hypotheses failed to reject. However, energy consumption was found to have a significant positive relationship with market return. It is, however, likely that this finding captures shared underlying drivers of Bitcoin’s price and its energy consumption, as well as possible reverse causality. Energy mix was found to have no significant effect on the three alternative outcomes, aligned with the fungibility of Bitcoin. Furthermore, regulatory was found not to significantly moderate also likely due to the narrow variation in the Indonesia’s scores during the study period. The study identified that markets do not reward sustainable mining with a market premium, implying that the transition towards renewable-powered mining in Indonesia requires more policy intervention.
The rapid development of the Fourth Industrial Revolution (IR 4.0) and Web3 has catalyzed digital transformation in the education sector through the convergence of Artificial Intelligence (AI) and the metaverse. However, most existing initiatives remain isolated and passive, lacking adaptive learning capabilities. These initiatives also face cross-platform interoperability constraints, cybersickness, gaps in educator readiness, and ethical concerns regarding biometric data privacy. In view of this, this study was conducted with the objective of constructing an integrated conceptual framework, Edu-AIMeta, that links technological dimensions with learning theories, critically analyzing implementation challenges, and proposing sustainable cyber-governance strategies. To achieve these goals, this study employs a conceptual-integrated literature review design by critically analyzing and synthesizing theoretical perspectives from 22 articles published between 2022 and 2026 across the Scopus and Web of Science (WoS) databases. The research findings outline three primary dimensions of integration success: the AI cognitive engine, the spatial metaverse environment, and user adoption and competence, particularly among Generation Z students and educators. Moving forward, this paper recommends the development of an inclusive meta-governance framework through the implementation of open-source standards, the formulation of ethical guidelines for biometric data protection, and the provision of structured immersive pedagogical training programs to ensure an equitable, secure, and sustainable future educational ecosystem.
Smart-contract vulnerabilities often arise from inconsistencies between business paths that should correspond to one another, such as single and batch entry points, direct and adapter-based flows, quote and execution paths, or inverse operations such as buy and sell. Existing analyzers are effective for many local syntactic and data-flow patterns, but they provide limited support for bugs whose oracle is relational: whether two semantically paired paths preserve compatible guards, state transitions, value flows, and failure behavior. This paper introduces chiral analysis, a relational model that treats paired business paths as implicit specifications for each other. We formalize chiral relations as static analogues of metamorphic relations, derive obligations over guards, actors, state, value, ordering, failure behavior, and external interactions, and report a vulnerability when a violated obligation has security impact. We implement this idea in ChiralDetector, a Solidity prototype that extracts business paths, ranks candidate pairs with static facts, applies LLM-based semantic filtering and detection, and validates and deduplicates findings. In a preliminary evaluation on the Phi protocol, ChiralDetector reduced 3,217 statically ranked path pairs to 1,643 semantic candidates, produced 101 deduplicated finding groups, and retained 44 strict-validator positives that manually collapsed to 13 effective unique issues. These include cross-art Merkle proof reuse, fee unit mismatches, public state-tracking helpers, and refund propagation gaps. The results suggest that chiral analysis can expose business-logic bug classes that are difficult to express as single-function rules while providing a structured way to control LLM cost and validator precision.
Predicting extreme price movements in high-frequency financial markets is a challenging task due to non-stationarity, heavy-tailed return distributions, and severe class imbalance. In particular, rare but impactful events are often difficult to detect using conventional modeling approaches, which typically treat extreme movements as isolated observations. This study proposes a volatility-aware approach for extreme event detection using high-frequency Bitcoin limit order book (LOB) data. Motivated by empirical evidence of volatility clustering, the target formulation is extended to incorporate both large future returns and high-volatility regimes. This redefinition increases the proportion of informative samples and aligns the learning objective with the underlying market dynamics. Using a tree-based model (XGBoost) with time-series cross-validation and imbalance-aware evaluation, the proposed method achieves a Precision-Recall AUC of approximately 0.40, significantly outperforming the baseline formulation with a PR-AUC of around 0.06. This represents more than a sixfold improvement in detecting rare events. The results highlight that target design plays a critical role in financial machine learning, often exceeding the impact of model complexity. By incorporating volatility structure into the labeling process, the proposed approach provides a more effective and realistic framework for extreme event detection in high-frequency cryptocurrency markets.
National currencies have long been associated with nation-state building and the expansion of state control. The rise of cryptocurrencies has the potential to disrupt state-society relations traditionally mediated through state-issued currencies. However, unregulated cryptocurrencies may be perceived as too unsafe to act as a true alternative to government-regulated currencies or investment vehicles. Cryptocurrency’s failures may instead lead people to appreciate the role of government more. Using the case of South Korea, we show that public discourse on cryptocurrencies has been more negative than positive in recent years. A demographically representative survey experiment reveals that exposing South Koreans to information about the volatility of cryptocurrencies increases their trust in government, as hypothesized. At the same time, exposure to positive information about cryptocurrencies does not undermine trust in government or support for government regulation. These results point to limitations of unregulated cryptocurrencies when it comes to eroding state-society relations.
G. Indirapriyadarsini, Sireesha Guttapalam, Ramyasri Mogarala, Kalpeshkumar L. Guptha
Background Maintaining optimal youth nutritional health is an urgent socio-economic imperative that underpins long-term human productivity and rights-based development. However, modern youth cohorts face unique dietary threats caused by the widespread availability of ultra-processed foods, targeted digital marketing, and complex food labeling protocols. Although Artificial Intelligence (AI) presents innovative avenues for personalized dietary profiling, existing systems remain largely technocentric and detached from statutory frameworks or behavioral realities. Objective This study bridges this interdisciplinary divide by evaluating a rights-based, technology-driven framework to improve youth nutritional health. It aims to: (1) empirically evaluate the “Knowledge-Attitude-Practice” (KAP) gap linking statutory consumer rights to real-world eating habits; (2) present the engineering design of a non-commercial Progressive Web Application (PWA) built to translate legal safeguards into daily behavioral changes; and (3) triangulate these findings using data from youth surveys and expert legal and nutritional panels. Methods Using a cross-sectional approach based on non-parametric power constraints, a validated survey instrument was completed by a target sample of Indian youth ( n = 354, aged 15–25 years). Concurrently, data matrices were compiled from regional legal experts ( n = 12) and public nutrition professionals ( n = 12) to cross-verify structural bottlenecks. Group variances, demographic dependencies, and rank associations were analyzed using robust non-parametric tests, including One-Way ANOVA, Kruskal-Wallis (H), Welch’s t-test, and Kendall’s Tau ( τ ) correlation coefficients. Results Inferential analysis revealed unexpected demographic trends: undergraduate status predicted significantly higher FSSAI safety awareness than post-graduate status ( p = 0.0037), while subjective health ratings exhibited a non-linear relationship with household income ( p = 0.0004), peaking in the lower-middle financial tier. Crucially, rank correlation testing revealed that the relationship between statutory knowledge and actual dietary actions is functionally non-existent ( τ = −0.001). This near-zero correlation provides clear empirical proof of a pronounced Knowledge-Action Gap, confirming that passive legal literacy fails to influence food selection in modern environments. Conclusion By framing automated behavioral interventions within the constitutional protections of Article 21 of the Constitution of India and the Consumer Protection Act, 2019, this study shows how the open-access PWA (nutrition-zb.pages.dev) can bridge this behavioral gap. This shifts the focus of consumer health informatics from basic self-tracking to a rights-based, systemic public health intervention.
Open access
Nutrition, Genetics, and Disease
Mobile Health and mHealth Applications
Artificial Intelligence in Healthcare and Education
The rapid digital transformation has made verifiable professional digital skills essential for workforce competitiveness, yet traditional resumes and certificates suffer from high fraud rates (50–70%), lengthy manual verification, and failure to recognize non-traditional pathways. This paper investigates SSI-, DID-, and W3C VC-based digital skills wallets as a solution to restore cryptographic trust in HR recruitment. Adopting the Design Science Research Methodology (DSRM), we conducted a PRISMA 2020 systematic review of 42 high-quality sources (2022-early 2026). The review established the technical maturity of SSI/VC technologies for micro-credentials and Learning and Employment Records (LERs) while revealing critical gaps in enterprise HR integration and emerging-market (particularly China) applications. We designed a modular, blockchain-optional digital skills wallet architecture fully compliant with W3C Verifiable Credentials Data Model v2.0, 1EdTech Comprehensive Learner Record, and China’s RealDID national identity infrastructure. The artifact supports lifelong credential aggregation, selective disclosure via BBS+ zero-knowledge proofs, and instant cryptographic verification (<3 seconds). The design was demonstrated through three China-specific recruitment use cases and empirically validated via a mixed-methods survey with 42 HR professionals and recruiters from major technology companies in Beijing, Shanghai, Shenzhen, and Guangzhou. Results indicated strong perceived utility: credential fraud was rated a major issue (M = 4.69), the wallet was expected to substantially reduce verification time (M = 4.57) and increase confidence in candidate claims (M = 4.45), with positive willingness to pilot or adopt (M = 4.19), especially when integrated with RealDID. These findings demonstrate that SSI-based digital skills wallets can near-eliminate resume fraud, collapse verification from weeks to seconds, expand talent pools through skills-first matching, and ensure privacy-preserving selective disclosure while aligning with national digital identity strategies. The study contributes a replicable DSRM template bridging verifiable credentials and skills-based talent management literatures, together with practical recommendations for HR leaders, ATS integration, and policy development.
This dataset accompanies the paper An Architectural and Empirical Study of Root-Only Zero-Knowledge Verification and contains the scripts, intermediate artifacts, and published results used to reproduce the empirical evaluation. The repository is organized around three experiment groups: On-chain verification — deployment and Groth16 proof verification on Ethereum Sepolia and zkSync Sepolia, including contract sources, Merkle-tree inputs, Groth16 proofs, and blockchain measurement CSVs and figures. Constraint-count comparison — Groth16 R1CS constraint counts and expanded PLONK gate counts for Merkle-tree depths 5–15, with measurement scripts and summary CSVs/figures. Proving-time comparison — off-chain Groth16 and PLONK proving benchmarks across depths 5–15, including proving scripts, generated witness/proof/key artifacts, and benchmark CSVs/figures. Shared setup files include Circom circuits, Merkle-tree preparation scripts, circuit inputs, and compiled circuit artifacts. Most of the generated data is produced by the provided scripts and does not need to be included separately if the reproduction pipeline is documented.
Open access
2 source records
Formal Methods in Verification
Physical Unclonable Functions (PUFs) and Hardware Security
ABSTRACT The emergence of decentralized compute-sharing protocols—peer-to-peer GPU and specialized-hardware marketplaces enabling firms to provision machine learning training and inference capacity without direct capital expenditure or on-balance-sheet lease recognition—has introduced a structurally novel form of operational leverage that conventional credit analysis is ill-equipped to detect. This paper investigates whether such off-balance-sheet utilization systematically distorts a firm's True Free Cash Flow to Firm (FCFF), defined here as reported FCFF adjusted for the capitalized economic equivalent of decentralized compute obligations, and quantifies the implicit tail-risk premium that credit default swap (CDS) markets demand for this hidden leverage. We formalize the problem in three stages. First, we construct a Hidden Leverage Ratio (HLR) by reconstructing the present value of a firm's implicit compute-sharing commitments from on-chain settlement data, smart-contract escrow balances, and protocol-level utilization telemetry, applying an exposure-graph methodology to map indirect exposure routed through special-purpose vehicles (SPVs) and protocol intermediary nodes. Second, we develop a structural credit risk model extending the classical Merton framework with a compound jump-diffusion component calibrated to compute-price volatility, in which hidden leverage enters the firm's effective asset volatility and default boundary as an unobserved but inferable state variable, generating a model-implied default probability and credit spread. Third, we empirically estimate the market-implied tail-risk premium by regressing observed 5-year CDS spreads against the constructed HLR across a panel of 412 firm-quarters drawn from technology, fintech, and AI-infrastructure issuers with active CDS markets, controlling for conventional leverage, profitability, and macro-credit factors. We find that CDS markets demand a statistically and economically significant tail-risk premium for hidden compute leverage: a one-standard-deviation increase in HLR is associated with a 61–142 basis point widening in 5-year CDS spreads depending on cohort, an effect that persists after controlling for reported leverage ratios, implying that CDS markets partially but incompletely price this off-balance-sheet exposure ahead of formal disclosure. The structural model achieves an R² of 0.87 against observed CDS spreads and reveals a convex, threshold-like premium structure consistent with jump-risk pricing rather than continuous Merton-style diffusion risk alone. We critically examine the limits of on-chain data observability, the endogeneity risk in inferring "true" cash flow from a credit-market-implied proxy, the accounting standard-setting implications for emerging digital lease constructs, and the systemic stability concerns raised by undisclosed, correlated compute leverage across the AI infrastructure sector. This work establishes a rigorous, empirically grounded framework at the convergence of decentralized finance infrastructure, structural credit risk theory, and corporate financial reporting.
Abstract Distributed ledgers – decentralized databases maintained by network consensus – are often modeled as directed acyclic graphs (DAGs) to capture the causal structure of data addition. Although blockchain systems like Bitcoin use linear chains, alternatives such as tangle in IOTA employ random DAGs. In such mechanisms each new transaction approves multiple predecessors selected through a randomized process. Prior work has established a fluid-limit approximation of the tangle’s growth, governed by a delay differential equation. In this paper we go beyond the fluid limit by analyzing the next-order behavior. We show that the fluctuations around the deterministic limit converge to a Gaussian process and derive a stochastic delay differential equation (SDDE) that describes this next-order approximation.
Federated Learning (FL) enables collaborative model training across decentralized participants without sharing raw data. However, existing FL systems remain vulnerable to Byzantine attacks and suffer from a lack of accountability, verifiability, and economic incentives for honest participation. We present BFL-Guard, a novel blockchain-orchestrated federated learning framework integrating: (i) zk-SNARK-based zero-knowledge gradient proofs, (ii) an on-chain Byzantine-tolerant aggregation smart contract, and (iii) a tokenized incentive protocol (FedToken). BFL-Guard stores model checkpoints as IPFS hashes anchored on Ethereum, ensuring tamper-evident auditability. Experiments on CIFAR-10 and Shakespeare benchmarks demonstrate 95.2% and 87.6% accuracy in IID and Non-IID settings, surpassing all baselines while converging 12.4% faster even under 30% Byzantine injection.
Elsir Ali Saad Mohamed, Khalid Ibrahim Abdelaziz Ishag, Omnia Salem, Ahd M. M. Abudraz · 8 authors
The convergence of blockchain technology and the Metaverse is redefining digital media ownership and distribution. Drawing on survey data from 613 digital media professionals and a qualitative synthesis of literature (2023–2026), this study examines how blockchain-based mechanisms—specifically non-fungible tokens (NFTs), smart contracts, and decentralized identity (DID) solutions—are associated with creator sovereignty and platform interoperability. Using a moderated chain mediation model within a socio-technical systems framework, the analysis shows that blockchain integration is associated with lower perceptions of platform dependency. This association is linked to a sequential pathway whereby higher decentralized governance is associated with lower intermediary control, which in turn is associated with higher creator monetization autonomy. Connectedness to decentralized protocols differentially shaped these processes: at the technical level, stronger protocol integration strengthened the negative association between blockchain adoption and intermediary dependence; however, at the governance level, a paradoxical pattern emerged, whereby stronger decentralization was associated with higher perceived governance overload in the context of algorithmic decision-making. By disentangling the technical and governance pathways, this study extends current understanding of digital media ecosystems beyond simple use-outcome associations. The findings highlight the importance of considering individual differences in digital literacy and institutional trust when designing blockchain governance frameworks. We conclude that blockchain is not merely an incremental improvement but a necessary architectural requirement for a resilient and equitable Metaverse, contingent upon addressing the risks of surveillance federalism and the digital divide.
The security and privacy of blockchain data have become critical research challeSimilarly, the full-function accounting node verifies the validitynges. While numerous approaches have been proposed to address these concerns, many existing schemes suffer from high computational complexity or excessive verification latency. To bridge this gap, this paper presents a secure and privacy-preserving blockchain data transaction verification system. By integrating the Paillier cryptosystem with a zero-knowledge range proof protocol, the proposed system ensures the confidentiality of transaction amounts and participant identities, simultaneously achieving strong anonymity and conditional traceability for users. Moreover, fully functional accounting nodes support efficient ciphertext-domain balance updates, eliminating the need for decryption during accounting operations. Experimental evaluation confirms the practicality and high performance of the proposed system.