Rana Muhammad Amir Latif, Farhan Ullah, Umar Raza, Leonardo Mostarda · 5 authors
As artificial intelligence (AI) becomes increasingly embedded in critical domains such as healthcare, cybersecurity, finance, and public services, the demand for ethical governance ensuring fairness, transparency, and accountability is urgent. Traditional oversight mechanisms often lack verifiable trust in decentralized and dynamic environments. This chapter introduces a Blockchain-Enabled Ethical Governance for AI (B-EGAI) framework that leverages blockchain's immutability, transparency, and smart contracts to enable auditable model registries, decentralized policy enforcement, and tamper-proof decision logs. By embedding ethical principles across the AI lifecycle, B-EGAI fosters accountability and trustworthiness in real-world applications. Case studies demonstrate its potential, while discussions on privacy-preserving computation, federated learning, and decentralized oversight outline future research, positioning blockchain as a foundation for trustworthy AI.
Blockchain Technology Applications and Security
Ethics and Social Impacts of AI
Artificial Intelligence in Healthcare and Education
Sika AGNONVI, Sèdjro Guillaume Nonkoudjè, Kpèdadon Louis Tèkpanzo, Yao Messah Kounetsron
Résumé Cette recherche analyse le rôle de la finance verte dans la promotion de l’entrepreneuriat durable au sein des petites et moyennes entreprises agricoles au Bénin. La méthodologie adoptée repose sur une approche qualitative fondée sur 46 entretiens semi-directifs réalisés auprès d’agripreneurs répartis dans cinq régions agricoles. L’analyse thématique des verbatims, conduite avec le logiciel NVivo 12, a permis de mettre en évidence une dynamique double. D’une part, la finance verte est perçue comme un levier stratégique pour accompagner la transition écologique et renforcer la résilience des exploitations face aux aléas climatiques, mais son accès reste limité par l’inadéquation des produits financiers existants, la rareté des subventions adaptées et l’absence de guichets verts décentralisés. D’autre part, l’entrepreneuriat durable se traduit par des pratiques telles que l’agriculture biologique, la conservation des sols, l’économie circulaire, la certification écologique et l’innovation verte, intégrant également des dimensions sociales comme l’implication des jeunes et des femmes. Les résultats révèlent une complémentarité forte dont l’accès à une finance verte adaptée facilite l’adoption des pratiques durables, tandis que ces dernières renforcent la crédibilité des PME agricoles auprès des institutions financières. Mots-clés : Finance verte, entrepreneuriat durable, PME agricoles, transition écologique. Abstract The objective of this research is to analyze the role of green finance in promoting sustainable entrepreneurship among small and medium-sized agricultural enterprises in Benin. The methodology adopted is based on a qualitative approach using 46 semi-structured interviews with agripreneurs in five agricultural regions. Thematic analysis of the transcripts, conducted using NVivo 12 software, revealed a dual dynamic. On the one hand, green finance is perceived as a strategic lever to support ecological transition and strengthen the resilience of farms in the face of climate hazards, but access to it remains limited by the inadequacy of existing financial products, the scarcity of appropriate subsidies, and the absence of decentralized green windows. On the other hand, sustainable entrepreneurship is reflected in practices such as organic farming, soil conservation, the circular economy, ecological certification, and green innovation, also incorporating social dimensions such as the involvement of young people and women. The results reveal a strong complementarity, with access to appropriate green finance facilitating the adoption of sustainable practices, while the latter reinforce the credibility of agricultural SMEs with financial institutions. Keywords: Green finance, sustainable entrepreneurship, agricultural SMEs, ecological transition.
Citra Fadhilah Utami, Arum Ira Nadhira, Clarisa Rofiati, Della Affesia Putri
Urban infrastructure financing in Indonesia faces a structural funding gap alongside rising subnational fiscal risk under decentralization. Therefore, this study aimed to develop a Multi-Criteria Decision Analysis framework to allocate loans across 50 priority cities in the 2025–2029 National Development Plan. The framework integrated fiscal capacity, debt sustainability, institutional readiness, public investment productivity, and spatial role into three composite indices, namely Soft Gate, Impact, and Priority. Using a weighted additive framework with percentile-based screening, cities were classified into Loan Priority, Blended, Grant, and Selective categories. The results showed that 28 percent qualified as Loan Priority, while 24 percent require blended mechanisms due to fiscal-impact mismatch. In conclusion, the framework enhances fiscal discipline and transparency in subnational borrowing decisions.
AIGP-Σ (AI Governance Protocol — Sigma) is a post-quantum cryptographic identity and authorization framework designed for autonomous AI agents operating in multi-agent and agentic payment environments. The protocol suite consists of five interconnected specifications: WP-01: Core Protocol — ML-DSA (CRYSTALS-Dilithium) based identity anchoring with STARK zero-knowledge proofs via RISC0, Bitcoin blockchain timestamping, and a cryptographic Kill Switch mechanism for emergency AI halt. WP-02: Kill Switch — Formal specification of the HALT proof system enabling verifiable, tamper-proof shutdown of AI agents without revealing operational state. WP-03: SSL for Agents — A mutual TLS-equivalent handshake protocol adapted for AI agent-to-agent communication, providing forward secrecy and post-quantum resistance. WP-04: Agentic Payments — Authorization layer for autonomous financial transactions executed by AI agents, with cryptographic scope limitation and audit trails. WP-05: Multi-Agent Orchestration — Trust propagation and delegation model for hierarchical multi-agent systems with verifiable credential chains.
Java applications are prone to vulnerabilities stemming from the insecure use of security-sensitive APIs, such as file operations enabling path traversal or deserialization routines allowing remote code execution. These sink APIs encode critical information for vulnerability discovery: the program-specific constraints required to reach them and the exploitation conditions necessary to trigger security flaws. Despite this, existing fuzzers largely overlook such vulnerability-specific knowledge, limiting their effectiveness. We present GONDAR, a sink-centric fuzzing framework that systematically leverages sink API semantics for targeted vulnerability discovery. GONDAR first identifies reachable and exploitable sink call sites through CWE-specific scanning combined with LLM-assisted static filtering. It then deploys two specialized agents that work collaboratively with a coverage-guided fuzzer: an exploration agent generates inputs to reach target call sites by iteratively solving path constraints, while an exploitation agent synthesizes proof-of-concept exploits by reasoning about and satisfying vulnerability-triggering conditions. The agents and fuzzer continuously exchange seeds and runtime feedback, complementing each other. We evaluated GONDAR on real-world Java benchmarks, where it discovers four times more vulnerabilities than Jazzer, the state-of-the-art Java fuzzer. Notably, an earlier GONDAR version contributed to Team Atlanta's first-place CRS in the DARPA AI Cyber Challenge, and is integrated into OSS-CRS, a sandbox project in The Linux Foundation's OpenSSF, to analyze open-source Java projects, where it has already uncovered a zero-day vulnerability.
We present a unified dynamical framework for the nontrivial zeros of the Riemann zeta function, integrating three perspectives: (i) the de Bruijn–Newman flow and its reduction to a logarithmic Coulomb gas, (ii) a renormalization group information flow from the 2C Theory, and (iii) spectral compression in 2D Dirac systems under strong magnetic fields. Through an iterative discovery process — connecting existing knowledge, identifying new principles at the intersection, then connecting those principles with prior knowledge to discover deeper ones — we identify three structural contributions: (1) The Disorder–Order Paradox: the irregularity of the prime distribution generates the information restoring force (curvature V''(1/2) = π²/8) that confines zeros to the critical line Re(s) = 1/2. (2) The Universal Irreversibility Threshold: the critical value C = 2/3, independently derived in D.S. Theory (holographic ratio β = 3/2), the 2C Theory (RG flow fixed point), and Lowest Landau Level physics (spectral weight threshold for forced Landauer erasure), marks the point at which one-dimensional spectral reduction becomes irreversible. (3) The Entropic Barrier: the information free energy V(σ) possesses a barrier surrounding σ = 1/2 whose height grows with integrated prime density, forbidding zero escape once the critical threshold is exceeded. We formulate one precisely stated open problem: proving that the entropic barrier height diverges as T → ∞, which is equivalent to establishing an L² + entropy → L∞ inequality for the equilibrium measure of the logarithmic gas. The framework connects analytic number theory, information theory, renormalization group methods, and condensed matter physics within a single coherent structure. This paper is a structural framework proposal, not a proof of the Riemann Hypothesis. The iterative discovery methodology is inspired by the WillCore simulation platform.
Prof. S. P. Palaskar, Swaraj Chikhale, Shantanu Chimote, Rakshit Sinha · 6 authors
Abstract— Traditional identity systems rely on centralized authorities, which creates single points of failure and privacy risks. We propose IDentix, a decentralized identity framework leveraging blockchain and cryptography to secure user credentials while preserving privacy. In IDentix, each user owns a self-sovereign identity (SSI) represented by a public/private key pair and a Decentralized Identifier (DID) registered on an Ethereum smart contract. Trusted issuers (e.g. governments, banks) provide verifiable credentials (VCs) to users off-chain, and users present cryptographic proofs (such as zero-knowledge proofs) of specific attributes to verifiers. Verifiers authenticate credentials by checking issuer signatures against public keys on the blockchain and querying an immutable credential registry. Our prototype on the Ethereum Sepolia testnet demonstrates that this design yields tamper-evident identity proofs without exposing personal data. As shown in prior work [1], blockchain-based SSI greatly reduces risks of identity theft while giving users full control over their data. Keywords— Blockchain; decentralized identity; self-sovereign identity; verifiable credentials; decentralized identifiers; identity verification; Ethereum; zero-knowledge proof.
This study presents ZK-EHR, a decentralized access control framework designed to enable secure and privacy-preserving sharing of encrypted electronic health records across institutional boundaries. Unlike existing blockchain-based EHR access control systems that expose user identities on-chain or lack cryptographic privacy guarantees, ZK-EHR decouples authorization from identity disclosure by integrating zk-SNARK-based proofs with blockchain smart contracts to verify policy compliance without revealing user roles, affiliations, or credentials. The framework employs three differentiated actor roles—Patient (Data Owner), Doctor (Care Provider), and Researcher (Authorized Analyst)—with distinct policy-driven access workflows, a custom Groth16 zero-knowledge circuit for role-based constraint enforcement, and a modular architecture combining on-chain verification with off-chain encrypted storage via IPFS. Concrete design proposals for access revocation and replay attack prevention are introduced to address operational security requirements. The system was evaluated under multiple operational and adversarial scenarios. Experimental results indicate consistent on-chain verification latency (approximately 390 ms), reliable rejection of tampered submissions, and per-verification gas consumption of 216,631 gas. A comparative analysis against representative baseline systems demonstrates that ZK-EHR uniquely combines identity anonymity, on-chain cryptographic policy enforcement, and auditable encrypted record retrieval. These findings establish the feasibility of zk-SNARK-based access control for decentralized, verifiable, and privacy-aware EHR management.
Transaction flow networks are crucial in detecting illicit activities such as wash trading, credit card fraud, cashback arbitrage fraud, and money laundering. Our collaborator, Grab, a leader in digital payments in Southeast Asia, faces increasingly sophisticated fraud patterns in its transaction flow networks. In industry settings such as Grab's fraud detection pipeline, identifying fraudulent activities heavily relies on detecting dense flows within transaction networks. Motivated by this practical foundation, we propose theS-T densest flow(STDF) query. Given a transaction flow networkG, a source setS, a sink setT, and a size thresholdk, the query outputs subsets$S^{\prime}\subseteq S$and$T^{\prime}\subseteq T$such that the maximum flow from$S^{\prime}$to$T^{\prime}$is densest, with$\vert S^{\prime}\cup T^{\prime}\vert\geq k$. Recognizing the NP-hardness of the STDF query, we develop an efficient divide-and-conquer algorithm,$\mathsf{Conan}$. Driven by industry needs for scalable and efficient solutions, we introduce an approximate flow-peeling algorithm to optimize the performance of$\mathsf{Conan}$, enhancing its efficiency in processing large transaction networks. Our approach has been integrated into Grab's fraud detection scenario, resulting in significant improvements in identifying fraudulent activities. Experiments show that$\mathsf{Conan}$, outperforms baseline methods by up to three orders of magnitude in runtime and more effectively identifies the densest flows. We showcase$\mathsf{Conan}$'s applications in fraud detection on transaction flow networks from our industry partner, Grab, and on non-fungible tokens (NFTs).
Margherita Cozzolino, Stephan Krenn, Thomas Lorünser
While QKD ensures information-theoretic security at the link level, real-world deployments depend on trusted repeaters, creating potential vulnerabilities. In this paper, we thus introduce a topology-hiding connectivity assurance protocol to enhance trust in quantum key distribution (QKD) network infrastructures. Our protocol allows network providers to jointly prove the existence of a secure connection between endpoints without revealing internal topology details. By extending graph-signature techniques to support multi-graphs and hidden endpoints, we enable zero-knowledge proofs of connectivity that ensure both soundness and topology hiding. We further discuss how our approach can certify, e.g., multiple disjoint paths, supporting multi-path QKD scenarios. This work bridges cryptographic assurance methods with the operational requirements of QKD networks, promoting verifiable and privacy-preserving inter-network connectivity.
This study investigates the role of artificial intelligence (AI) tokens in dynamic interactions, diversification, and hedging capabilities, in relation to non-fungible tokens (NFTs), decentralised finance (DeFi) tokens, and renewable energy assets. Using the Time-Varying Parameter Vector Autoregressive (TVP-VAR) model, we examine return, volatility, and higher-order spillovers across both time and frequency domains. The results show that NFTs serve as persistent channels for the transmission of return and volatility shocks, driven by their speculative nature. AI and renewable tokens primarily absorb systemic risk due to their lower liquidity and niche adoption. DeFi tokens play flexible roles, shifting between transmitters and receivers across market regimes. The results demonstrate asset-specific idiosyncrasies and that volatility spillovers are generally stronger than return spillovers. Frequency-domain analysis highlights that digital tokens dominate short-term spillovers, while renewable assets absorb shocks across horizons. However, higher-order moment results reveal that extreme risk linkages shift transmission channels. Our results also confirm that oil market (OVX) shocks drive short-term return connectedness, CBOE volatility (VIX) volatility, and policy uncertainty (EPU) significantly impact return linkages. The results of our portfolio analysis show that AI tokens form the core of diversification, NFTs provide short-term speculative hedging, and renewable assets, particularly solar-linked tokens, act as low-cost stabilisers, underscoring the need for active rebalancing under different market regimes. These findings provide meaningful implications for policymakers, regulators, and portfolio managers for strengthening systemic risk oversight and considering asset-specific idiosyncrasies in investment strategies.
This paper describes a zero-knowledge proof system that enables verification of password policy compliance within an asymmetric password-authenticated key exchange (aPAKE) protocol specifically OPAQUE (RFC 9807) without revealing the password to the server. The system is built on a composable sub-circuit architecture: independent verification gadgets are combined into a single zero-knowledge proof, each gadget accepting portions of the private witness and producing public instance values, enabling the server to verify multiple password properties in one proof verification. Four gadgets are disclosed: (1) a Policy Engine for character class verification via lookup tables, (2) a History Nullifier for password inequality proof via squared-difference accumulation, (3) an OPAQUE Binder for cryptographic binding to the aPAKE registration element via hash-to-curve and elliptic curve scalar multiplication, and (4) a Breach Detector for offline breached-password detection via Bloom filter non-membership proof using algebraic hashing. The composable architecture permits addition of further gadgets without modifying existing ones, each extending the public instance vector.
Laila Khalid, Muhammad Usman Akhtar, Muhammad Khalid, Iftikhar Ahmed
The evolving technology in AI and distributed systems requires ethical concepts of how sensitive data can be verified without breach of privacy. Conventional AI systems present the following critical concerns: exposure of data, breach of privacy, and ethical issues concerning transparent but confidential computation. This chapter is a full-fledged cryptographic proof, Zero-Knowledge Proofs (ZKPs), which makes it possible to deploy AI ethically by verifying privacy. The framework is supported by mathematical underpinnings to enable model validation and training verification, as well as federated learning without the underlying datasets or parameters of the models. The chapter shows that ZKPs can be used to meet ethical AI without compromising privacy. It can be used in healthcare, finance, and voting systems where ethical concerns require verification and confidentiality. This chapter offers a new method of dealing with core ethical dilemmas in AI systems and safeguarding privacy and security in algorithmic decision-making exercises.
Today, many computing workloads are executed in loosely coupled, geographically distributed environments where resources are owned by different organizations. Examples include inter-institutional research infrastructures, community-operated clusters, and edge deployments. As disconnections are frequent in such environments, ensuring reliable task execution remains a fundamental challenge. Kubernetes, the de facto standard for cluster orchestration, provides centralized control and strong consistency, but suffers from slow recovery when node failures occur frequently. At the opposite extreme, blockchain-based orchestration removes centralized control but incurs substantial latency due to global consensus, making it unsuitable for time-sensitive task scheduling. This paper presents Mutual Cloud, a decentralized orchestration framework that operates between these two extremes. Mutual Cloud adopts a hybrid architecture where task admission and queue management are handled in a centralized manner similar to conventional public clouds, whereas most scheduling functions, including execution-node selection and failure handling, are performed in a decentralized manner by autonomous agents using a distributed hash table. We implement a prototype of Mutual Cloud and evaluate its performance through large-scale simulation studies. The results show that Mutual Cloud maintains stable performance comparable to centralized baselines under normal conditions while achieving approximately five-second-level recovery latency under substantial node failures.
Gifar Arif Haryadi, Allwinnaldo, Muhammad Rasyid Redha Ansori, Jae-Min Lee · 5 authors
PureRx is an innovative blockchain-based prescription management system designed to overcome the limitations of traditional paper prescriptions, including medication errors, fraud, and inefficiencies in tracking patient records. By leveraging Non-Fungible Tokens (NFTs) and batch minting, PureRx enables efficient, secure, and patient-centric control of prescription data. Patients maintain ownership of their medical records, while healthcare providers and pharmacies benefit from transparent and immutable prescription workflows. Experimental evaluation shows that PureRx improves operational efficiency by 37.18% in initial cycles, increasing to 51% in subsequent cycles compared to existing systems, while reducing costs by 46.76% in high-volume scenarios. Security analysis confirmed that no critical vulnerabilities were found and that all prescription issuance and claiming events are tamper-resistant and auditable on-chain. By combining scalability, transparency, and patient empowerment, PureRx demonstrates the potential of blockchain to transform prescription management in modern healthcare systems.
The rising frequency of cyber threats increases the need for incident reporting that is transparent, efficient, and privacy-preserving. This study designs and implements a hybrid Web2-Web3 cyber incident reporting prototype that anchors report references on a blockchain while storing full incident details off-chain, and explores non-fungible token (NFT) recognition incentives for reporters. Using an SDLC-based iterative prototyping approach, we built a React single-page application integrated with a Laravel REST API and MySQL for off-chain storage, and deployed Solidity smart contract modules on the Arbitrum Sepolia testnet to record report identifiers and UUID pointers (dataPointer) and to mint NFTs after administrative validation. We conducted black-box functional testing across core scenarios (submission, storage, pointer anchoring, validation, and minting) and a user acceptance study with 25 participants (15 cybersecurity students and 10 IT practitioners) using a 5-point Likert questionnaire. All tested scenarios executed as expected in the test environment, and on-chain events were traceable to corresponding backend records via transaction receipts and logged identifiers. The acceptance evaluation yielded an overall mean score of 3.4/5 (about 68%), indicating moderate acceptance and supporting the work as a prototype feasibility study rather than organizational-level generalization. The prototype demonstrates a practical workflow for hybrid incident reporting with transaction-level traceability and recognition incentives; future work should strengthen cryptographic binding (e.g., content hashing) and validate the approach with CSIRT stakeholders in operational settings.
ABSTRACT This article explores the application of demurrage money, a concept developed by Silvio Gesell, into Web3. Demurrage money, designed to discourage the hoarding of currency and prevent economic stagnation and concentrations in wealth, offers a potential remedy for the problems of traditional fiat and gold‐backed monetary systems. The article presents an overview of Web3, highlighting its core principles such as being decentralized, permissionless, community governed, and programmable. It critiques the limitations of current Web3 cryptocurrencies, particularly Bitcoin and other networks that have emerged since. By design these networks enable excessive asset storage and face sustainability challenges such as governance centralization and inadequate ecosystem funding. The article proposes that the implementation of a network coin tax, as a form of demurrage, would help to incentivize productive economic activity, decentralize coin ownership, provide reliable funding for node operators and ecosystem development and create opportunities for large‐scale public goods funding. Various monetary supply models are discussed, evaluating their compatibility with demurrage systems. The article concludes that demurrage based economic systems could lead to more resilient, equitable and sustainable Web3 ecosystems that have significant potential for making a global societal impact.
Daily probability changes in Kalshi macro prediction markets forecast cryptocurrency realized volatility through two distinct channels. The monetary policy channel, measured by Fed rate repricing on KXFED contracts, predicts Bitcoin volatility in sample with t = 3.63 and p < 0.001 but exhibits regime dependence tied to the 2024-2025 rate-cutting cycle. The recession risk signal from KXRECSSNBER proves more stable out of sample, delivering an MSFE ratio of 0.979 with Clark-West p = 0.020. The inflation channel, measured by CPI repricing on KXCPI contracts, predicts altcoin volatility for Ethereum, Solana, Cardano, and Chainlink with t-statistics ranging from -2.1 to -3.4 and out-of-sample gains for Ethereum at MSFE = 0.959 with p = 0.010 and Solana at p = 0.048. Both the Bitcoin--Fed-dovish and Chainlink--CPI specifications survive Benjamini-Hochberg correction at q = 0.05. Orthogonalization and baseline comparisons against Fed Funds futures, Treasury yields, and the Deribit implied volatility index confirm that these signals carry information not embedded in conventional financial instruments. The sample covers ten Kalshi event series and six cryptocurrency assets over January 2023 to March 2026.
We study asynchronous alignment, a first-class multimodal learning setting in which a dense primary stream must be fused with sporadic external context whose value depends on when it arrives. Unlike standard multimodal benchmarks that assume structural synchrony, this setting requires models to reason explicitly about freshness and trust. We focus on the event-conditioned case in which continuous market states are paired with delayed web intelligence, and we use high-frequency cryptocurrency markets only as a timestamped, high-noise stress test for this broader problem. We propose CGCMA (Conditionally-Gated Cross-Modal Attention), whose central design principle is to separate text-conditioned grounding from lag-aware trust control. Text first attends over price sequences to identify event-relevant market states, after which a conditional gate uses modality agreement, web features, and lag $τ_{\mathrm{lag}}$ to regulate residual injection and fall back toward unimodal prediction when external context is stale or contradictory. We introduce CMI (Crypto Market Intelligence), an asynchronous evaluation corpus with 27,914 real-news samples pairing high-frequency price sequences with lagged web intelligence. On the current short real-news corpus, CGCMA attains the highest mean downstream Sharpe ratio ($+0.449 \pm 0.257$) among the evaluated baselines under a shared zero-cost threshold-trading evaluation on news-available bars. Additional controls show that the gain is not explained by web scalars alone and is not recovered by simple freshness heuristics. The resulting evidence supports problem validity and a promising asynchronous multimodal gain on this stress-test setting.
Smart contract vulnerabilities can cause substantial financial losses due to the immutability of code after deployment. While existing tools detect vulnerabilities, they cannot effectively repair them. In this paper, we propose SCPatcher, a framework that combines retrieval-augmented generation with a knowledge graph for automated smart contract repair. We construct a knowledge graph from 5,000 verified Ethereum contracts, extracting function-level relationships to build a semantic network. This graph serves as an external knowledge base that enhances Large Language Model reasoning and enables precise vulnerability patching. We introduce a two-stage repair strategy, initial knowledge-guided repair followed by Chain-of-Thought reasoning for complex vulnerabilities. Evaluated on a diverse set of vulnerable contracts, SCPatcher achieves 81.5\% overall repair rate and 91.0\% compilation pass rate, substantially outperforming existing methods.