FibRace, jointly developed by KKRT Labs and Hyli, was the first large-scale experiment to test client-side proof generation on smartphones using Cairo M. Presented as a mobile game in which players proved Fibonacci numbers and climbed a leaderboard, FibRace served a dual purpose: to engage the public and to provide empirical benchmarking. Over a three-week campaign (September 11-30, 2025), 6,047 players across 99 countries generated 2,195,488 proofs on 1,420 unique device models. The results show that most modern smartphones can complete a proof in under 5 seconds, confirming that *mobile devices are now capable of producing zero-knowledge proofs reliably*, without the need for remote provers or specialized hardware. Performance was correlated primarily with RAM capacity and SoC (System on Chip) performance: devices with at least 3 GB of RAM proved stably, when Apple's A19 Pro and M-series chips achieved the fastest proving times. Hyli's blockchain natively verified every proof onchain without congestion. FibRace provides the most comprehensive dataset to date on mobile proving performance, establishing a practical baseline for future research in lightweight provers, proof-powered infrastructure, and privacy-preserving mobile applications.
Andrei Seoev, Leonid Gremyachikh, Anastasiia Smirnova, Yash Madhwal · 11 authors
In blockchain networks, the strategic ordering of transactions within blocks has emerged as a significant source of profit extraction, known as Maximal Extractable Value (MEV). The transition from spam-based Priority Gas Auctions to structured auction mechanisms like Polygon Atlas has transformed MEV extraction from public bidding wars into sealed-bid competitions under extreme time constraints. While this shift reduces network congestion, it introduces complex strategic challenges where searchers must make optimal bidding decisions within a sub-second window without knowledge of competitor behavior or presence. Traditional game-theoretic approaches struggle in this high-frequency, partially observable environment due to their reliance on complete information and static equilibrium assumptions. We present a reinforcement learning framework for MEV extraction on Polygon Atlas and make three contributions: (1) A novel simulation environment that accurately models the stochastic arrival of arbitrage opportunities and probabilistic competition in Atlas auctions; (2) A PPO-based bidding agent optimized for real-time constraints, capable of adaptive strategy formulation in continuous action spaces while maintaining production-ready inference speeds; (3) Empirical validation demonstrating our history-conditioned agent captures 49\% of available profits when deployed alongside existing searchers and 81\% when replacing the market leader, significantly outperforming static bidding strategies. Our work establishes that reinforcement learning provides a critical advantage in high-frequency MEV environments where traditional optimization methods fail, offering immediate value for industrial participants and protocol designers alike.
Federated learning promises to revolutionize machine learning by enabling collaborative model training without compromising data privacy. However, practical adaptability can be limited by critical factors, such as the participation dilemma. Participating entities are often unwilling to contribute to a learning system unless they receive some benefits, or they may pretend to participate and free-ride on others. This chapter identifies the fundamental challenges in designing incentive mechanisms for federated learning systems. It examines how foundational concepts from economics and game theory can be applied to federated learning, alongside technology-driven solutions such as blockchain and deep reinforcement learning. This work presents a comprehensive taxonomy that thoroughly covers both centralized and decentralized architectures based on the aforementioned theoretical concepts. Furthermore, the concepts described are presented from an application perspective, covering emerging industrial applications, including healthcare, smart infrastructure, vehicular networks, and blockchain-based decentralized systems. Through this exploration, this chapter demonstrates that well-designed incentive mechanisms are not merely optional features but essential components for the practical success of federated learning. This analysis reveals both the promising solutions that have emerged and the significant challenges that remain in building truly sustainable, fair, and robust federated learning ecosystems.
Byzantine Fault-Tolerant (BFT) consensus protocols ensure agreement on transaction ordering despite malicious actors, but unconstrained ordering power enables sophisticated value extraction attacks like front running and sandwich attacks - a critical threat to blockchain systems. Order-fair consensus curbs adversarial value extraction by constraining how leaders may order transactions. While state-of-the-art protocols such as Themis attain strong guarantees through graph-based ordering, they ask every replica to re-run the leader's expensive ordering computation for validation - an inherently symmetric and redundant paradigm. We present AUTIG, a high-performance, pluggable order-fairness service that breaks this symmetry. Our key insight is that verifying a fair order does not require re-computing it. Instead, verification can be reduced to a stateless audit of succinct, verifiable assertions about the ordering graph's properties. AUTIG realizes this via an asymmetric architecture: the leader maintains a persistent Unconfirmed-Transaction Incremental Graph (UTIG) to amortize graph construction across rounds and emits a structured proof of fairness with each proposal; followers validate the proof without maintaining historical state. AUTIG introduces three critical innovations: (i) incremental graph maintenance driven by threshold-crossing events and state changes; (ii) a decoupled pipeline that overlaps leader-side collection/update/extraction with follower-side stateless verification; and (iii) a proof design covering all internal pairs in the finalized prefix plus a frontier completeness check to rule out hidden external dependencies. We implement AUTIG and evaluate it against symmetric graph-based baselines under partial synchrony. Experiments show higher throughput and lower end-to-end latency while preserving gamma-batch-order-fairness.
Tehnologija ulančanih blokova pruža siguran, efikasan i transparentan način spremanja podataka kao i rukovanja kriptovalutama. Pomoću Rust programskog jezika i Anchor okvira, razvijen je pametni ugovor za spremanje podataka o kupljenim kartama za vlak na Solana plaformi. Kako bi se omogućila daljnja komunikacija s pametnim ugovorom nakon njegovog objavljivanja, razvijena je mobilna aplikacija pomoću programskog jezika Kotlin u okruženju Android Studio.
Cryptocurrency ATMs have become a preferred payment method for scammers because they are a fast, easy, and often hard to trace way to get access to a victim's cash. 2 Yet, a large majority of U.S. adults cannot recognize cryptocurrency ATMs (i.e., they're unable to distinguish them from traditional bank ATMs).This di culty in recognizing cryptocurrency ATMs is especially pronounced among adults ages 50 and older.
The security and privacy of vehicle-to-everything (V2X) communication are critical for the reliability of future intelligent transportation systems (ITS). Existing V2X public key infrastructure (VPKI) models face centralization risks, inefficient revocation, limited privacy, and vulnerability to quantum attacks. While blockchain-based frameworks improve decentralization, they often depend on classical cryptography and offer limited defense against Sybil attacks and identity linkage. This paper presents DIMA, a post-quantum decentralized identity management architecture for V2X authentication and privacy preservation. DIMA integrates a dual-layer permissioned blockchain with CRYSTALS-Dilithium for quantum-resistant digital signatures, zk-STARKs for anonymous pseudonym issuance, and privacy tokens with a reputation-based refresh mechanism for Sybil resistance. A hash-based accumulator supports scalable revocation, while self-sovereign identity (SSI) and zero-knowledge attribute proofs enable privacy-preserving access control. Security is analyzed under a quantum-capable adversarial model, and performance is evaluated using analytical benchmarks. Results demonstrate that DIMA achieves strong unlinkability, scalable Sybil deterrence, and real-time authentication, addressing key limitations of prior approaches and providing a quantum-resilient foundation for next-generation V2X ecosystems.
Since more users are moving to cloud computing, keeping our data safe and private now matters more. Since most cloud storage is run from just one location, it is easier for attackers and causes issues if the system fails. We come up with a new way to secure cloud storage by using blockchain technology and smart contracts. Blockchain mainly allows us to have a safe and distributed record that ensures data access can be trusted. By using smart contracts, we enable people to safely access data without any help from a middleman. It means that you can see every use of data access in the system, and these actions cannot be deleted or changed. Using blockchain technology with distributed storage, we ensure that everything happening is recorded and access is regulated correctly. We also discuss a case study to illustrate how our idea helps with transparency and trust, while at the same time mentioning concerns such as how far it can be used and problems with regulations, and our solutions for these issues.
Cross-chain interoperability is essential for the next generation of decentralized finance applications, yet existing bridges suffer from security weaknesses, high latency, and fragmented trust models. This paper introduces SnapBridge, a protocol that transfers assets across heterogeneous blockchains using cryptographic state snapshots combined with optimistic verification. A snapshot aggregator collects Merkleized proofs of account states and transaction histories from the source chain. Instead of verifying all proofs on-chain, SnapBridge relies on optimistic execution: transfers proceed immediately but can be challenged within a fraud-proof window. Fraud detection is performed by light clients using succinct verification rules. We implement SnapBridge across Ethereum, Polygon, and Avalanche testnets and benchmark transfer throughput, failure handling, and gas consumption. Results show up to 3× improvement in transfer latency and a 40% reduction in on-chain verification cost compared to multisig-based bridges. The paper evaluates adversarial scenarios such as corrupted aggregators, delayed snapshots, and chain reorgs. SnapBridge provides a modular, safer alternative for cross-chain liquidity flows.
Modern economic ecosystems require radical hazard management systems that may take care of big streams of statistics without compromising on regulatory compliance and business transparency. Conventional batch-based risk assessment models exhibit intrinsic shortcomings in addressing millisecond-level market turbulence and intricate network interdependencies that define new trading environments. Sophisticated artificial intelligence platforms embedded in distributed computing environments offer transformational possibilities for real-time risk sensing and mitigation. The suggested architecture develops end-to-end risk analytics capacity via ensemble machine learning algorithms, graph contagion analysis, and explainable AI features to meet strict regulatory demands. Complex data pipelines ingest heterogeneous finance streams from worldwide exchanges, payment networks, and blockchain ledgers in tandem. Tailored graph neural networks examine systemic risk transmission patterns in connected financial institutions while retaining dynamic relationship mapping capabilities. Explainable AI integration presents version interpretability and regulatory adherence through function attribution strategies and robust audit trail retention. Cloud-local infrastructure layout helps elastic scaling throughout multi-cloud environments using fault-tolerant distributed orchestration systems. Performance assessments display large upgrades in detection latency and predictive accuracy relative to standard batch-processing strategies. The design embodies a paradigm shift towards forward-looking, adaptive, and transparent risk management functionality critical to ensuring financial stability in progressively complex market conditions
Asif Mahmood, Razib Hayat Khan, Jahid Hasan Rony, M. M. Mahbubul Syeed · 5 authors
The rapid growth of digital services has made it essential to have secure, transparent, and decentralized platforms to support reliable and efficient service exchanges in recent time. This study presents a distributed blockchain-based framework that facilitates trustworthy and reputation-driven interactions between service providers and receivers. Service providers can register and promote their services, while users can browse, search, and request offerings through a decentralized and transparent network in this system. Each transaction is immutably recorded on a blockchain ledger, ensuring tamper-proof documentation of service details, including transaction ID, participants, status, timestamp, and review content. The platform also uses a dynamic reputation system where user feedback generates a score that influences future service interactions for both the service provider and the receiver. A proof-of-concept prototype was developed and evaluated to demonstrate the feasibility of integrating this blockchain-based distributed peer-to-peer system into serviceoriented digital platforms. The experimental results validate the system's capacity to handle diverse transaction types while maintaining performance, scalability, and user satisfaction. This approach improves accountability, fosters trust, and mitigates fraud in peer-to-peer digital marketplaces.
Lê Thái Hùng, Huu-Han Nguyen, Thai Hung Van, Doan Minh Trung · 5 authors
Smart contract vulnerabilities pose serious risks in blockchain ecosystems, yet existing detection methods often rely on either source code or opcode analysis in isolation, missing complementary information across modalities. This paper presents a multimodal learning framework that combines semantic features extracted from source code using CodeBERT with Structure-Based Traversal (SBT) encoding and behavioral patterns derived from opcode sequences using a gMLP(gated Multi-Layer Perceptron) model applied to TF-IDF vectors. The framework systematically evaluates various fusion strategies, including concatenation, self-attention, cross-attention, and a hybrid attention mechanism, all within a unified architecture and dataset. Extensive experiments on the SmartBugs benchmark demonstrate two key findings: (1) the pairing of CodeBERT(SBT) and gMLP(opcode) achieves superior modality synergy (F1-score: 0.84), and (2) our hybrid attention fusion mechanism further improves performance to 0.87 F1, outperforming other fusion strategies by up to 3.6%. Compared to the best unimodal baselines, our approach yields a 12.8% F1 gain. To the best of our knowledge, this is the first study to provide a systematic benchmark of these fusion strategies under a unified framework for smart contract vulnerability detection. These results underscore the importance of informed modality selection and intelligent fusion design in building robust AI-driven vulnerability detection tools.
Tuan-Dung Tran, Huynh Phan Gia Bao, Tra Minh Trong, Nguyen Tan Cam · 5 authors
Integrating decentralized identity (DID) systems with state authorities introduces complex challenges related to trust, privacy, and auditability. The Lotus Bridge framework addresses these by proposing a hybrid digital identity architecture suitable for national-scale deployment. It integrates a permissioned Proof-of-Authority (PoA) blockchain for sovereign credential issuance with a cross-chain verification bridge that utilizes zero-knowledge proofs to enable privacy-preserving selective disclosure. This architecture is one of the first to combine state-backed issuance with interoperable, private cross-chain verification in a unified system. Two core protocols—state-anchored issuance and cross-chain verification—are formally defined and implemented in a working prototype. Experimental results demonstrate strong performance: cryptographic proofs remain under 600 bytes and end-to-end verification latency consistently stays below 2 seconds, enabling real-time applicability. Additionally, parallelization reduces proof generation time by over 90%, and the system achieves significant cost efficiency, with on-chain verification starting at 244 Gwei per credential and scaling to 14K Gwei for 1,000, offering up to 75% cost savings compared to existing Ethereum and Polygon solutions. These findings establish Lotus Bridge as a scalable and costeffective foundation for sovereign digital identity in cross-chain ecosystems.
This research investigates the integration of Artificial Intelligence (AI) and blockchain technologies to develop a more robust and adaptive cybersecurity framework. Amid the growing complexity and frequency of cyber threats, traditional security systems are increasingly insufficient in ensuring data integrity, threat detection, and operational transparency. The study aims to explore how the synergy between AI and blockchain can address these limitations and enhance digital security infrastructures. A qualitative exploratory approach was employed, utilizing a Systematic Literature Review (SLR) of 42 peer-reviewed articles published between 2020 and 2025. The analysis revealed three dominant integration models: AI-based anomaly detection with blockchain-secured logging, smart contracts for automated incident response, and blockchain-based identity verification enhanced by AI behavioral analysis. The proposed framework demonstrated a high detection rate (94.3%), low response latency (0.7 seconds), and improved auditability compared to state-of-the-art approaches. These findings suggest that combining AI's predictive capabilities with blockchain’s immutable and decentralized architecture offers a more comprehensive cybersecurity solution. However, challenges such as computational overhead, energy consumption, and interoperability issues remain. The study concludes that the integrated approach not only enhances resilience and transparency but also provides a scalable foundation for future cybersecurity systems, especially in critical sectors such as healthcare, finance, and government services.
In this article, we carry out a comprehensive comparative legal analysis of the criminal policy in the field of cryptocurrency confiscation in Russia, the European Union, and the United States. The relevance of this research is determined by the rapid growth of crimes involving crypto assets (money laundering, cybercrimes, and drug trafficking) and the lack of effective mechanisms for their final confiscation and implementation in Russia, which undermines the efforts of law enforcement agencies. We aim to identify effective models of cryptocurrency confiscation based on a comparative analysis of legislation and practice in leading jurisdictions and, on this basis, to develop recommendations for improving the Russian legal framework. The methodology includes a comparative legal analysis of regulatory acts (Russian Criminal Procedure Code, EU Directive 2014/42/EU, US Code), a formal legal method, an analysis of judicial practice (Russia, USA), and doctrinal sources. The key findings can be summarized as follows: (1) the USA enjoys the most advanced system, where the U.S. Marshals Service (USMS) actively uses private exchanges to convert confiscated assets; (2) the EU has established a strong legal framework (5/6AMLD, Directive 2014/42/EU); however, implementation practices here vary among member states, combining government-owned storage and outsourced sales through licensed platforms; (3) in the Russian Federation, despite the practice of seizure and arrest of crypto assets and legislative initiatives, the legal mechanism for their confiscation and sale is lacking, making court decisions unenforceable. In order to overcome this gap in Russia, it is necessary to urgently legislate cryptocurrency as property for the purposes of confiscation in the Criminal Procedure Code of the Russian Federation, grant the Federal Service for Judicial Enforcement of the Russian Federation the authority to sell through licensed platforms, as well as to develop expert potential. Our study extends the current knowledge by detailing the technological aspects of confiscation in the EU and the USA and proposes specific ways to modernize the criminal policy of the Russian Federation.
Effective ICT governance is essential in the public sector to drive digital transformation and improve service delivery. This research investigates the corporate governance of ICT Policy Framework (CGICTPF) and Public Finance Management Act (PFMA) and State Information Technology Agency (SITA) Act governs the operational activities and strategic directions of Government Information Technology Officers (GITOs) in Eastern Cape, KwaZulu-Natal and Free State provincial administrations in South Africa. Using a comparative case study, the research draws on policy analysis and interviews to reveal governance obstacles in procurement and executive ICT engagement. KwaZulu-Natal shows progress due to strong leadership, while Eastern Cape and Free State face delays from compliance-driven cultures and bureaucracy. The study urges a balance between regulation and agility, recommending GITO empowerment through decentralized procurement and leadership development. It advances ICT governance theory by exposing multi-level implementation challenges.
Abdullah Ayub Khan, Abdul Khalique Shaikh, Roobaea Alroobaea, Abdullah M. Baqasah · 7 authors
Advanced precision oncology has the potential to revolutionize the current infrastructure of precision oncology, especially in cancer diagnosis and ongoing monitoring, owing to the strong development and advancement of the Internet of Medical Things (IoMT) and Blockchain Distributed Ledger Technology (BDLT). In order to improve cancer diagnosis accuracy and real-time patient monitoring, this paper introduces a novel Blockchain-enabled secure IoMT architecture that incorporates state-of-the-art multi-modal data fusion algorithms, such as weighted fusion. A comprehensive picture of patient health is made possible by this proposed architecture, which presents a novel mechanism for the safe, dynamic aggregation of various datasets, including as genetic portfolios, medical imaging, and wearable sensory-enabled data, as we assess the existing solutions. However, a BDLT-enabled immutable distributed ledger that uses cutting-edge encryption techniques to protect patient privacy while guaranteeing data immutability, decentralized access controls, fine-grained data availability, and traceability are among the main goals. This proposed architecture's unique context-aware data fusion algorithm greatly outperforms traditional techniques, achieving a diagnostic accuracy of 97.10%, precision of 98.25%, F1-scroe of 0.97, and sensitivity of 96.85%. Furthermore, the incorporation of BDLT enhanced security and privacy protection by eliminating single points of failure and attaining 100% immutability. When handling, organizing, and processing dynamic data, a latency of less than 250 ms is calculated. Through simulations using real-world case studies, the proposed work is tested, enhancing the system's reliability while also showcasing its scalability, energy efficiency, and robustness. Based on the examination of the simulation findings, we are able to reach parameters such as data integrity, throughput exceeding 300 transactions per second, and resource utilization efficiency optimized up to 85% in comparison to other state-of-the-art methodologies. It guarantees dependable functioning even with fluctuating computational loads. The findings demonstrate its ability to provide precise, secure, and useful insights instantly, revolutionizing real-time monitoring and cancer diagnosis.
Move to Earn (MtoE) initiatives emerged during the COVID-19 pandemic. STEPN, the leading MtoE walking applications (app.) using various artificial intelligence (AI) techniques, allows users to earn rewards by walking, jogging, or running with their mobile devices while wearing Non-Fungible Token (NFT) shoes. Although MtoE games have huge potentials to improve our quality of life (QOL), there is no scientific papers to point out the possibilities. This work describes present situation of MtoE and importance of AI to establish MtoE projects, and proposes several potential applications of MtoE, including as an aid to support athletes, international students, and bedridden individuals. This is the primitive survey research of MtoE projects as a trigger to start scientific discussions.
This paper proposes a novel solution for securing digital image ownership and verification within the Non-Fungible Token (NFT) ecosystem. While existing blockchain systems lack adequate protection for intellectual property, the proposed system employs watermarking to preserve copyrights during NFT minting. It also integrates Merkle Trees for efficient duplication detection and counterfeit prevention. Additionally, the system can identify tampered regions, enhancing duplicate NFT detection. These contributions provide a secure and scalable framework for protecting digital content in the evolving NFT landscape.
Advanced Steganography and Watermarking Techniques
The rapid decentralization and digitalization of local electricity markets have introduced new cyber-physical vulnerabilities, including key leakage, data tampering, and identity spoofing. Existing blockchain-based solutions provide transparency and traceability but still depend on classical cryptographic primitives that are vulnerable to quantum attacks. To address these challenges, this paper proposes Q-EnergyDEX, a zero-trust distributed energy trading framework driven by quantum key distribution and blockchain. The framework integrates physical-layer quantum randomness with market-level operations, providing an end-to-end quantum-secured infrastructure. A cloud-based Quantum Key Management Service continuously generates verifiable entropy and regulates key generation through a rate-adaptive algorithm to sustain high-quality randomness. A symmetric authentication protocol (Q-SAH) establishes secure and low-latency sessions, while the quantum-aided consensus mechanism (PoR-Lite) achieves probabilistic ledger finality within a few seconds. Furthermore, a Stackelberg-constrained bilateral auction couples market clearing with entropy availability, ensuring both economic efficiency and cryptographic security. Simulation results show that Q-EnergyDEX maintains robust key stability and near-optimal social welfare, demonstrating its feasibility for large-scale decentralized energy markets.
Son, Do Hai, Hieu, Le Vu, Khoa, Tran Viet, Alem, Yibeltal F. · 8 authors
Blockchain technology has experienced rapid growth and has been widely adopted across various sectors, including healthcare, finance, and energy. However, blockchain platforms remain vulnerable to a broad range of cyberattacks, particularly those aimed at exploiting transactions and smart contracts (SCs) to steal digital assets or compromise system integrity. To address this issue, we propose a novel and effective framework for detecting cyberattacks within blockchain systems. Our framework begins with a preprocessing tool that uses Natural Language Processing (NLP) techniques to transform key features of blockchain transactions into image representations. These images are then analyzed through vision-based analysis using Vision Transformers (ViT), a recent advancement in computer vision known for its superior ability to capture complex patterns and semantic relationships. By integrating NLP-based preprocessing with vision-based learning, our framework can detect a wide variety of attack types. Experimental evaluations on benchmark datasets demonstrate that our approach significantly outperforms existing state-of-the-art methods in terms of both accuracy (achieving 99.5%) and robustness in cyberattack detection for blockchain transactions and SCs.
New technologies, such as blockchain, are designed to address various system weaknesses, particularly those related to security. Blockchain can enhance numerous aspects of traditional banking systems by transforming them into digital, immutable, secure, and anonymous ledger. This paper proposes a new banking application ALBank, which is based on blockchain and smart contract technologies. Its functionality relies on invoking functions within smart contracts deployed on the Ethereum blockchain. This approach enables decentralization and enhances both security and trust. In this context, the paper first presents a critical analysis of existing research on blockchain and traditional banking systems, with a focus on their respective challenges. It then examines the Know Your Customer (KYC) process and its various models. Finally, it introduces the design and development of ALBank, a decentralized banking application built on the Ethereum blockchain using smart contracts. The results show that the integration of blockchain and smart contracts effectively addresses key issues in traditional banking systems, including centralization, inefficiency, and security vulnerabilities by storing critical data on a decentralized, immutable ledger, managing processes autonomously, and making transactions transparent to all users.
Maximal Extractable Value (MEV) refers to a class of attacks to decentralized applications where the adversary profits by manipulating the ordering, inclusion, or exclusion of transactions in a blockchain. Decentralized Finance (DeFi) protocols are a primary target of these attacks, as their logic depends critically on transaction sequencing. To date, MEV attacks have already extracted billions of dollars in value, underscoring their systemic impact on blockchain security. Verifying the absence of MEV attacks requires determining suitable upper bounds, i.e. proving that no adversarial strategy can extract more value (if any) than expected by protocol designers. This problem is notoriously difficult: the space of adversarial strategies is extremely vast, making empirical studies and pen-and-paper reasoning insufficiently rigorous. In this paper, we present the first mechanized formalization of MEV in the Lean theorem prover. We introduce a methodology to construct machine-checked proofs of MEV bounds, providing correctness guarantees beyond what is possible with existing techniques. To demonstrate the generality of our approach, we model and analyse the MEV of two paradigmatic DeFi protocols. Notably, we develop the first machine-checked proof of the optimality of sandwich attacks in Automated Market Makers, a fundamental DeFi primitive.