Proof-of-work blockchains purchase their security through the expenditure of compute and energy â yet the work performed is itself discarded entirely. Decentralized AI networks provide useful compute but secure no ledger. Myelin unifies both functions: miners jointly operate a large agentic language model (the network model) via pipeline parallelism, and the same cryptographically attested inference work (âProof of Inferenceâ, PoI) determines compensation and feeds the voting weight of consensus. The native coin MYL closes the value cycle: users burn MYL for inference credits, and miners receive newly minted MYL in proportion to verified work (burn-and-mint equilibrium). We specify (i) a layered architecture that decouples consensus latency from inference latency, (ii) a three-tier verification model combining deterministic redundancy, optimistic sampling with a bisection game, and optional zkML anchors, (iii) a token economy with a quantifiable security condition (S_min = g/pÂČ), and (iv) core data types and reference algorithms of an open-source implementation. We name the open core problems â deterministic cross-hardware inference, the latencyâcollusion trade-off of pod formation, and the 50% redundancy overhead â explicitly and propose measurement procedures. Bilingual release: this record contains the English and German editions of the whitepaper (PDF + Markdown each). In case of discrepancies, the German original prevails.
Layer 2 scaling solutionsâincluding payment-channel-based Lightning Networksand rollup-based off-chain execution environmentsâare commonly understood aslinear scaling projects for blockchain transaction throughput. This paper proposesan alternative structural interpretation: the emergence of Layer 2 is not a continuous increase in system capacity, but a percolation phase transition that occurswhen the density of off-chain channels or cross-rollup connections crosses a critical threshold. During this phase transition, the system shifts from a fragmentedlocally connected state to a globally routable giant connected state. The paperanalyzes the Lightning Network and the rollup ecosystem as comparative cases.Empirical studies of the Lightning Network show that its scale-free topology forcescritical hub nodes to bear a disproportionate connection load, thereby binding thenetworkâs global connectivity to the survival of a few high-centrality nodes. Therollup ecosystem faces the structural predicament of liquidity fragmentation, and itsevolution toward cross-rollup interoperability likewise exhibits a phase-transitionlogic from quantitative change to qualitative change in network effects. Based onthe above analysis, this paper distills three design principles for Layer 2 scalability:facilitating the institutionalization of cross-domain connections, avoiding overlyhomogenized cognitive convergence, and implementing differentiated verificationrouting among tasks with different security requirements.
Byzantine Fault Tolerance (BFT) consensus is a foundational achievement indistributed systems theory, providing dual guarantees of safety and liveness forasynchronous networks with malicious nodes. However, this theoretical frameworkimplicitly relies on a presupposition that has not been sufficiently examined: allhonest nodes are homogeneous in their cognition of the protocolâsobjectives. Whena decentralized system evolves from a closed task-oriented network into an opengovernance ecosystem, the functional differentiation of nodes in storage strategies,verification preferences, and governance commitments deprives this presuppositionof descriptive validity. This paper does not deny the security contributions of BFT,but argues that security alone is insufficient to constitute a complete consensus.The full logic of consensus requires a complementary dimension: the capacity toaccommodate functional differentiation. Integrating recent empirical classificationstudies of blockchain nodes, protocol architecture design experiences that acknowledge functional differentiation, and Ostromâs polycentric governance theory, thispaper proposesâCognitive Niche Equilibriumâ(CNE) as an extension of the consensus concept. System stability does not require all nodes to be isomorphic inevery function; rather, it requires the simultaneous satisfaction of three stabilityconditions: feedback anchoring, cross-validation, and evolutionary stability. Using Bitcoin and Ethereum as comparative cases, this paper translates these threeconditions into a layered implementation architecture symbiotic with existing BFTprotocol stacks, and discusses the security engineering principles and trade-offsunder this framework.
The Wireless Sensor Networks (WSN) and the Internet of Things (IoT) have revolutionized various application areas such as smart cities, health, industrial automation, environment, agriculture, and intelligent transportation systems. Despite the successful widespread use of WSNs-IoT, they still have several security issues including resource constrained sensor nodes, decentralized design, and the combination of heterogeneous communication protocols and insecure wireless communication channels. Most traditional security solutions including cryptographic methods, intrusion detection systems are rule-based, which is not enough to protect against the advanced, evolving and zero-day attacks. Therefore, the paradigm of artificial intelligence (AI) has become an exciting approach to creating intelligent, adaptive and autonomous cyber security solutions. This paper is a systematic literature review of the security solutions based on artificial intelligence (AI) applied to WSNs (WSNs) in the context of IoT. Structured review methodology is followed in the study, which critically analyzes recent machine learning, deep learning, reinforcement learning, federated learning, blockchain and edge intelligence advancements in the field of intrusion detection, anomaly detection, threat prediction, authentication, privacy preservation, and secure communication. These approaches are compared on the basis of their accuracy to detect the target, computational complexity, energy efficiency, scalability, and privacy and feasibility for deployment in resource constrained environments. Moreover, it classifies the already known security threats, examines layer-wise defense measures and analyzes upcoming hybrid AI frameworks, which combine several intelligent technologies. The review reveals several gaps in the research, such as the lack of explainability of models, use of benchmark datasets, susceptibility to adversarial and model-poisoning attacks, blockchain scalability issues, and the absence of standardized, secure system architectures that can offer reliable, privacy-preserving, and energy-efficient protection. The paper then proposes future research directions that highlight the need of combining Explainable Artificial Intelligence (XAI), Federated Learning, Blockchain, and Edge AI for the construction of strong and adaptive cybersecurity frameworks. This review is a comprehensive reference for researchers and practitioners who are interested in designing secure, intelligent and sustainable WSN-IoT systems for next-generation cyber-physical ecosystems.
Blockchain can secure and verify electronic health records (EHRs) for multi-institution healthcare systems, but Layer-1 storage costs and throughput limitations make full on-chain EHR storage impractical. A new model is proposed named FZRP (Federated-ZK-Rollup Pipeline). It is a hybrid methodology combining Federated Learning (FL), off-chain storage (IPFS), zk-rollup batching with adaptive batch sizing, and parallel proof pipelines to minimize per-record transaction cost while preserving auditability and privacy. Using a synthetic dataset of 50,000 EHRs, it quantifies cost reductions under realistic assumptions and demonstrate orders-of-magnitude per-record savings. A formal cost model, latency and security analyses, and sensitivity studies are provided. The experimental evaluation demonstrates that adaptive batching significantly reduces per-record transaction cost under conservative Layer-1 cost assumptions to as low as $0.000024, achieving over 99.999% cost reduction while maintaining scalability and privacy. The limitations, regulatory considerations, and paths for future work are discussed.
Shapour Shiranifar, Sayyed Mohammad Reza Davoodi, Saeid Aghasi
Purpose: This study aimed to identify and validate the factors influencing the transfer of Blockchain-based services in the digital transformation process of banks. The present study seeks to answer the fundamental question of how a comprehensive framework can be designed to facilitate the successful adoption and implementation of Blockchain technology in banking environments.Methodology: This study used a mixed qualitative approach combining content analysis and the Delphi method. In the first phase, semi-structured interviews were conducted with 20 experts from Saderat Bank of Iran, fintech companies, and IT service providers. The resulting data were analyzed using thematic content analysis, yielding 17 key factors across six thematic areas. These factors were evaluated, and a consensus was reached across three Delphi rounds involving 15 experts. The Delphi process ended with an agreement level of 80% as a reliability criterion.Findings: Components such as interoperability (92%), scalability (88%), digital governance (94%), change management capacity (90%), and customer-centric innovation (91%) play a pivotal role in successful technology transfer. It was found that Blockchain adoption alone will not lead to improved performance unless strong dynamic capabilities and organizational readiness support it. Among the most critical challenges identified are resistance to change (85%), inadequate technical infrastructure (82%), regulatory challenges (79%), and data privacy restrictions (87%). Based on the final findings, success in Blockchain technology transfer requires simultaneous attention to three key dimensions: a) the technical dimension by prioritizing system interoperability and solution scalability; b) the organizational dimension by focusing on developing dynamic capabilities and creating a culture of innovation; c) the environmental dimension by reforming regulatory frameworks and developing security standards. It is suggested that banks invest in specialized employee training, develop a data governance strategy, and partner with fintech startups to pave the way for the successful implementation of this technology.Originality/Value: This study bridges the gap between academic literature and management practice by providing a conceptual and validated framework for decision-makers. The principal value of this research is to outline the essential components for effective and sustainable technology transfer in digitally evolving institutions. This framework can be used as a guide for banks and financial institutions in successfully implementing Blockchain-based solutions. The findings of this study can also serve as a basis for future research on innovative applications of Blockchain in the financial services sector.
Suman Bijapur, Shilpa Patil, Parimala, Shantala P H
With unparalleled threats to the integrity of digital information, democratic practices, and public confidence in media, deepfake technology comprises a new class of harm. Deep generative models can be used to generate realistic looking (and sounding) fake human faces and voices, which is great news for bad actors who seek to spread misinformation, commit crimes and ruin journalism. CyberLink Fights Deepfakes with New AI Model That Uses Neural Network Traditional methods used to identify deepfakes have depended on centralised AI systems that can't be trusted at face value and there is little or no way of proving a piece of content's authenticity. In this work, we have presented a solution that involves multi-modal deepfake detection and has utilized learning-based forgery detection framework to be deployed on blockchain for evidence tamper resistance. The proposed framework employs a hybrid CNN-RNN architecture that computes facial, audio and metadata feature in parallel to detect unseen deepfakes with accuracy of 94.2%, compared to the single-modal baselines (CNN only: 81.3%, and audio only: 67.4%). Novelty: Blockchain timestamping with cryptographically secured certificates of authenticity for third-party verification while protected proprietary detection logic is not revealed. At the computational efficiency and bandwidth threshold required for edge deployment, video processing at 30 FPS and only 2.1 MBs makes this applicable on any average mobile device. It holds for 12k synthetic videos (celebrities, politicians, newscasters) and diverse deepfake generation methods (FaceSwap, DeepFaceLab, StyleGAN). Societal impact: framework mitigates $1.2T annual disinformation damage and champions digital rights through decentralized verification. Via mashable.com Framework addresses the convergence of deepfake detection, blockchain authentication and the case for sustainable cybersecurity: As a global community grapples with synthetic media in ways we've never seen before, support online safety experts to respond.
Open access
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Generative Adversarial Networks and Image Synthesis
The increasing adoption of blockchain technology has transformed digital transaction systems by providing secure, decentralized, and transparent data management. The vehicle procurement process, however, still relies heavily on conventional procedures involving multiple intermediaries, manual documentation, and lengthy verification mechanisms that often increase operational costs and expose transactions to fraudulent activities. This paper presents a blockchain-enabled smart vehicle procurement framework that modernizes the complete purchasing lifecycle while preserving transaction integrity and user trust. The proposed system utilizes blockchain technology as an immutable distributed ledger for securely storing vehicle records, ownership history, buyer credentials, and transaction information. Smart contracts are employed to automate critical activities including buyer verification, ownership transfer, payment authorization, and regulatory validation without requiring manual intervention. The decentralized architecture minimizes dependency on third-party agencies while improving transparency, reducing processing delays, and enhancing security against data manipulation. Since every transaction is permanently recorded on the blockchain, both buyers and sellers can independently verify the authenticity of vehicle records before completing a purchase. The proposed framework maintains the same operational workflow and implementation strategy as the reference system while offering improved documentation quality and technical presentation. Experimental observations demonstrate that blockchain-assisted procurement significantly improves transaction efficiency, strengthens security, simplifies ownership transfer, and establishes a reliable digital marketplace for modern automotive commerce. The framework represents a scalable solution capable of supporting future intelligent transportation systems and smart mobility applications.
Counterfeit and substandard medicines remain a major global public health threat, with the World Health Organization estimating that up to 10% of medicines in low- and middle-income countries are falsified or substandard, exceeding 20% for some therapeutic classes in sub-Saharan Africa. Blockchain technology, with its decentralised, immutable, and transparent digital ledger architecture, has been proposed as a promising solution for strengthening pharmaceutical supply chain traceability, yet little is known about the readiness of community pharmacies in Nigeria to adopt it. This study assessed the knowledge, current practices, and implementation readiness of blockchain-based traceability for counterfeit medicine prevention among community pharmacists in Bayelsa State, Nigeria, and examined the barriers and enablers influencing adoption. A descriptive cross-sectional survey design was employed, using a structured, validated questionnaire administered electronically through the Association of Community Pharmacists of Nigeria (ACPN), Bayelsa State Chapter. A total of 119 valid responses were obtained, exceeding the minimum sample size derived from Yamane's and Cochran's formulae, representing a 79.3% response rate. Data were analysed using descriptive and inferential statistics, including Chi-square tests. Findings revealed that although 71.4% of respondents had heard of blockchain technology, mean knowledge scores across core blockchain concepts were uniformly low (1.26-1.72 on a 5-point scale), reflecting a substantial awareness-comprehension gap. Authentication practices were overwhelmingly manual, with 77.3% relying on visual inspection of NAFDAC numbers and only 9.2% using digital scanning; 68.9% of pharmacists had encountered suspected counterfeit medicines in the past year. Implementation readiness was below average (Mean = 2.70/5.0), driven by strong training willingness (Mean = 3.74) but deficient infrastructure (Mean = 1.80). Resistance to change and high cost were the leading barriers, while user-friendly applications and mandatory regulation were the strongest enablers. The study concludes that community pharmacies in Bayelsa State are not yet structurally ready for blockchain-based traceability, although a receptive attitudinal environment and strong professional motivation exist to support a phased transition. It is recommended that NAFDAC and the Pharmacists Council of Nigeria develop a phased regulatory framework and integrate blockchain literacy into continuing professional development, that government prioritise infrastructure investment, and that technology developers design mobile-first, user-friendly, offline-capable systems, to enable a coordinated transition toward blockchain-enabled pharmaceutical traceability in Nigeria.
Mario Rusev, Rafael Schmidt, Edward Lambe, Christian Schmieder · 5 authors
International organizations including the Bank for International Settlements (BIS) have adopted SDMx (The standard for Statistical Data and Metadata) as the standard for exchanging official statistics. Trust in published data is essential for evidence-based policymaking. This paper shows how binding each SDMx dataset to its source using blockchain technology can enhance confidence in official statistics. We present a proof of concept implemented on the XRP Ledger (XRPL) and contribute, as an integrated whole, (i) an SDMx-native canonicalization and per-< Series > hashing pipeline, (ii) a domain-separated Merkle aggregation scheme for batched anchoring, (iii) a self-contained, identity-bound verification artefact in which the SDMx message itself carries both the ordered Merkle leaves and a W3C Verifiable Credential signed by a publisher identity key cryptographically bound to the publisherâs XRPL address via an on-chain attestation registry, so any consumer can re-derive the anchored root and verify the publisherâs identity from the file alone plus a single ledger lookup, (iv) an open-source XRPL-based reference implementation, and (v) a cost model that captures the batch-size / latency / fee trade-off and is solved for an economically optimal batch size. The system enables near-real-time data verification, provides cryptographic integrity guarantees, and establishes a foundation for future extensions, including zero-knowledge proofs and automated verification by AI agents. Measurements on the prototype show median publication latency of 3â5 s and verification latency of 1â2 s under the controlled test conditions described in Section 7. The approach is data-format-agnostic and can be extended to other structured statistical or regulatory formats.
The advent of fault-tolerant quantum computing represents the most significant and schedulable threat to the cryptographic foundations of blockchain infrastructure. Over $3.2 trillion in digital assets are currently secured by RSA, Elliptic Curve Cryptography (ECC), and ECDSA: algorithms provably broken by Shor's algorithm running on a Cryptographically Relevant Quantum Computer (CRQC). The Harvest Now, Decrypt Later (HNDL) threat means this risk is not future-dated. Adversaries with archival capability are already harvesting public blockchain data for retrospective decryption. In August 2024, NIST published three finalized post-quantum cryptographic standards: FIPS 203 (ML-KEM), FIPS 204 (ML-DSA), and FIPS 205 (SLH-DSA). In 2025, NIST standardized HQC, providing code-based cryptographic diversity alongside the lattice-based primary algorithms. These standards are mandated for U.S. national security systems under NSA CNSA 2.0 and for high-risk sector operators in the EU under the EU PQC Roadmap. This paper introduces QubitChain.io: a natively quantum-safe Layer 1 blockchain implementing all four NIST post-quantum standards from genesis block. The protocol employs hardware Quantum Random Number Generator (QRNG) entropy at both key generation and consensus randomness levels, and introduces Proof of Quantum Entropy (PoQE), a novel consensus mechanism whose validator selection cannot be predicted or manipulated by any adversary regardless of computational capability. The paper provides the complete technical, economic, and governance specification for the QubitChain.io protocol, covering cryptographic architecture, QRNG system design, consensus mechanism, network protocol, tokenomics, governance, and regulatory compliance.
Agentic AI networking (AgentNet) systems rely heavily on third-party skillset implementations and distributed multi-agent collaboration, yet they face major claim-to-capability inconsistencies and security vulnerabilities under trust-by-declaration assumptions. To bridge this gap, this paper proposes TrustAgentNet, a dual-tier blockchain-secured zero-trust framework. Specifically, a global Chain of Skillsets (CoS) governs the lifecycle of skillset metadata with protocols empowered by specialized agents to enforce off-chain auditing while maintaining lightweight on-chain cryptographic consensus. Furthermore, transient, task-oriented Chains of Collaboration (CoC) are dynamically established to enable trustless distributed multi-agent collaboration. Theoretical analysis of the three-way trade-off among security level, task performance, and resource overhead is provided and empirically validated. Experimental results on a hardware prototype demonstrate that compared with no-blockchain trust-by-default baselines, the zero-trust overhead of TrustAgentNet is dominated by off-chain inference, while the blockchain layer incurs minor ledger costs via the ledger-IPFS storage and on/off-chain integration design. Crucially, the proposed verification pipeline achieves a flawless 100% accuracy across 50 AI models, correctly validating 40 honest skillsets and intercepting 10 adversarial ones, and generalizes to non-AI domains with an 83.91% accuracy and a 0.85 F1-score across 1478 features from 171 ClawHub skills. Adversarial experiments further show that TrustAgentNet enables autonomous skillset self-recovery against various malicious attacks.
Rukhsar Zaka, Faiza Irfan, Sidra Rehman, Muhammad Ahsan Hayat
Cryptocurrency markets are highly volatile, nonlinear, and affected by several internal and external market factors, making price forecasting a challenging task. Accurate cryptocurrency price forecasting can support investors, traders, and financial analysts in making informed decisions. This research paper presents a comparative analysis of machine learning and deep learning models for cryptocurrency price forecasting using historical Aave (AAVE) cryptocurrency data. The dataset consists of 275 records and 10 features, including Date, High, Low, Open, Close, Volume, and Marketcap. The Close price is selected as the target variable, while High, Low, Open, Volume, and Marketcap are used as predictor variables. Five models are implemented and compared: Linear Regression, Support Vector Regression, Random Forest Regressor, XGBoost Regressor, and Long Short-Term Memory. The models are evaluated using Mean Absolute Error, Root Mean Square Error, Mean Absolute Percentage Error, R-squared score, and directional accuracy. Experimental results show that the LSTM model achieved the best performance with the lowest RMSE of 2.74, MAE of 1.78, MAPE of 3.91%, and R-squared score of 0.965. The results indicate that deep learning models, especially LSTM, are more suitable for capturing temporal dependencies and nonlinear patterns in cryptocurrency price data.
Verifiable Random Functions (VRFs) are cryptographic primitives that generate unpredictable randomness together with a publicly verifiable proof of correct generation following the protocol, a critical requirement for decentralized applications in blockchain infrastructure, decentralized finance, and online gaming. While distributed VRFs (DVRFs) eliminate dependence on a single trusted authority, existing constructions face a fundamental dilemma: linear proof sizes in the threshold parameter (DDH-DVRF) or reliance on computationally expensive bilinear pairings (GLOW-DVRF, FlexiRand). This thesis resolves both facets of this dilemma with three contributions despite the downside of adding another interaction round amongst the parties involved in the generation. We first introduce DVRFwCP, a distributed VRF with constant-size, pairing-free proofs achieved by layering a threshold structure over a Chaum-Pedersen NIZK system, where we use an augmented secure distributed key generation to produce the required nonce. We then introduce Icy-DVRF, which eliminates the quadratic interaction bottleneck of DVRFwCP by integrating the FROST-style preprocessed nonces, reducing total per-evaluation communication from O(n^2 t) to O(t) while preserving constant-size proofs. Finally, we introduce IcyVeil, the first pairing-free output-private DVRF, which extends Icy-DVRF with a Schnorr-based blinding mechanism that conceals the VRF output until revealed by the user. We evaluate all three constructions theoretically and empirically via Solidity smart-contract implementation on the Ethereum Sepolia testnet. The measurements show a 43.02% reduction in on-chain verification gas cost compared to GLOW-DVRF.
This chapter explores the transformative role of Financial Technology (FinTech) in advancing green finance, a crucial component in addressing global sustainability challenges. By integrating technologies such as blockchain, artificial intelligence (AI), big data analytics, and smart contracts, FinTech facilitates the efficient allocation of capital towards sustainable projects. The chapter examines how these innovations enhance transparency, optimize risk assessment, and enable decentralized financing models like peer-to-peer energy trading and tokenization. Additionally, it addresses the challenges posed by greenwashing, market volatility, and regulatory uncertainty, while highlighting the future opportunities for growth in green finance. Ultimately, the chapter underscores the potential of FinTech to drive systemic change and promote a low-carbon, sustainable economy.
This chapter explores the transformative potential of blockchain and artificial intelligence (AI) in revolutionizing green finance. It begins by examining the role of digital transformation in driving sustainable financial practices, highlighting the integration of blockchain and AI. The chapter delves into blockchain's applications in enhancing transparency, traceability, and security within green finance, particularly through smart contracts and decentralized finance solutions. It further discusses AI's contributions to improving risk assessment, ESG evaluation, and combating greenwashing. The synergies between blockchain and AI are explored, showing how their combined use optimizes sustainability-focused investments. Additionally, the chapter addresses regulatory and ethical considerations surrounding these technologies. Finally, it discusses emerging trends and opportunities in green finance, providing insights into the future of sustainable financial systems driven by technological innovation.