With the growing number of decentralized finance (DeFi) applications, transaction fairness in blockchains has gained much research interest. As a broad concept in distributed systems and blockchains, fairness has been used in different contexts, varying from ones related to the liveness of the system to ones that focus on the received order of transactions. In this work, we revisit the fairness definitions and find that existing fairness definitions are not adapted to blockchains with multiple DApps. We then provide a more generic one calledverifiable fairness. Compared with prior definitions, our notion has two unique features: (i) it relaxes the ordering rules to apredicate; (ii) it enables users to independently verify if their transactions comply with the predicate for concrete applications. We also provide a scheme that achieves verifiable fairness, leveraging trusted hardware. Unlike prior works that usually design a dedicated consensus protocol to achieve fairness, our scheme can be integrated with any blockchain system. Our evaluation results on Amazon EC2 using up to 120 instances across different regions show that our construction imposes only minimal overhead on existing blockchain systems.
Cross-chain decentralized finance ( DeFi) applications enable the seamless transfer of assets and data across diverse blockchain networks, thereby enhancing liquidity and user flexibility. However, the distributed nature and inter-operability of these networks introduce significant security challenges, ranging from double-spending and smart contract vulnerabilities to mismatches in consensus mechanisms and privacy risks. In this survey, we introduce a novel review about the recent literature that explores these multifaceted challenges. We categorize the research based on attack vectors and the security requirements of cross-chain systems, discuss state-of-the-art solutions including advanced cryptographic techniques and rigorous auditing practices, and outline open problems that warrant further explorations. Our work provides a comprehensive analysis of the current security landscape in cross-chain DeFi and emphasizes future research directions.
Physical Unclonable Functions (PUFs) and Hardware Security
Olaolu Samuel Adesanya, Akindamola Samuel Akinola, Lawrence Damilare Oyeniyi
Smart contract technologies are revolutionizing the landscape of global finance by enabling secure, automated, and transparent cross-border financial transactions. Traditional international transactions are often hindered by delays, high costs, and reliance on multiple intermediaries such as correspondent banks, clearinghouses, and regulatory bodies. These processes not only increase operational complexity but also expose transactions to risks of fraud, errors, and regulatory inefficiencies. Smart contracts, built on blockchain platforms, address these challenges by embedding contractual terms directly into self-executing code that autonomously enforces obligations once pre-defined conditions are met. This technological innovation eliminates the need for third-party verification, reduces transaction latency, and ensures that funds or assets are exchanged only when contractual conditions are satisfied, thereby enhancing both trust and efficiency in global markets. The application of smart contracts in cross-border financial transactions streamlines settlement processes by providing real-time execution and verification, reducing the risk of human error and dispute. Automated compliance mechanisms can be integrated into the contract logic, ensuring adherence to international trade and financial regulations while minimizing manual oversight. Moreover, transparency inherent in blockchain technology allows stakeholders including regulators, financial institutions, and clients to access immutable transaction records, strengthening accountability and trust. The interoperability of smart contract platforms with emerging technologies such as digital currencies and decentralized finance ecosystems further amplifies their potential to reshape international trade and investment flows. Strategically, smart contracts promote inclusivity in global markets by lowering transaction costs, expanding access for small and medium-sized enterprises, and accelerating settlement times, particularly in regions where traditional banking infrastructure is underdeveloped. However, challenges remain, including legal recognition across jurisdictions, standardization of protocols, and ensuring resilience against cyber threats. Addressing these issues through coordinated governance and international regulatory cooperation will be critical for mainstream adoption. In summary, smart contract technologies enable secure, automated, and transparent cross-border financial transactions, offering significant advances in efficiency, cost reduction, and trust-building across global economic markets. Keywords: Smart Contracts, Blockchain, Cross-Border Transactions, Financial Automation, Global Economic Markets, Transparency, Compliance, Decentralized Finance.
With the rapid expansion of digital knowledge platforms and intelligent information systems, organizations and communities are producing a vast number of unstructured knowledge data, including annotated corpora, technical diagrams, collaborative whiteboard content, and domain-specific multimedia archives. However, knowledge sharing across institutions is hindered by privacy risks, high communication overhead, and fragmented ownership of data. Federated learning promises to overcome these barriers by enabling collaborative model training without exchanging raw knowledge artifacts, but its success depends on motivating data holders to undertake the additional computational and communication costs. Most existing incentive schemes, which are based on non-cooperative game formulations, neglect unstructured interactions and communication efficiency, thereby limiting their applicability in knowledge-driven scenarios. To address these challenges, we introduce SC-NBTI, a smart contract and Nash bargaining-based incentive framework for federated learning in knowledge collaboration environments. We cast the reward allocation problem as a cooperative game, devise a heuristic algorithm to approximate the NP-hard Nash bargaining solution, and integrate a probabilistic gradient sparsification method to trim communication costs while safeguarding privacy. Experiments on the FMNIST image classification task show that SC-NBTI requires fewer training rounds while achieving 5.89% higher accuracy than the DRL-Incentive baseline.
Andrew Hudson‐Smith, Duncan Wilson, Valerio Signorelli
This chapter explores the complex interplay between economics and security in the rapidly evolving metaverse. It examines the economic dynamics underpinning virtual worlds, including the rise of cryptocurrencies, non-fungible tokens, and digital asset ownership. We examine case studies of economic systems within platforms like Second Life, Decentraland, and Fortnite, highlighting the substantial real-world value generated in these virtual economies. The chapter also addresses the critical security challenges facing the metaverse, including cybercrime, terrorism, and jurisdictional issues in enforcing laws across digital borders. It recounts one of the earliest documented virtual terrorist attacks and discusses the need for robust security measures to protect users and digital assets. We conclude by emphasising the frontier nature of the metaverse, its potential for economic growth and innovation, and the critical importance of balancing security concerns with the development of this new digital frontier.
ChipmunkRing, a practical post-quantum ring signature construction tailored for blockchain environments. Building on our Chipmunk lattice-based cryptographic framework, this implementation delivers compact digital signatures ranging from 20.5 to 279.7KB, with rapid signing operations completing in 1.1-15.1ms and efficient validation processes requiring only 0.4-4.5ms for participant groups of 2-64 members. The cornerstone of our approach is Acorn Verification-a streamlined zero-knowledge protocol that supersedes the classical Fiat-Shamir methodology. This innovation enables linear O(n) authentication complexity using concise 96-byte cryptographic proofs per participant, yielding a remarkable 17.7x performance enhancement for 32-member rings when compared to conventional techniques. Our work includes rigorous mathematical security demonstrations confirming 112-bit post-quantum protection (NIST Level 1), extensive computational benchmarking, and comprehensive support for both standard anonymity sets and collaborative threshold constructions with flexible participation requirements.
The speedy development of the Internet of Things (IoT) needs safe and efficient and scalable data transmission systems that can resist privacy violation and inefficiency within the network. The paper introduces a blockchain-based IoT transmission framework that inculcates Paillier Homomorphic Encryption (PHE) to end-to-end data transmission security as well as Practical Byzantine Fault Tolerance (PBFT) consensus mechanism as a low-latency trust establishment methodology. The model has been written in Python and tested with the Intel Lab IoT Sensor Dataset. The proposed system has an average success rate in secure data encryption of up to 99.97 percent and acceptable percentages of bit error with tolerance limits on all packet lengths (1 KB and above) as depicted in experimental results, thus with low computation needs (3.2 ms encryption, 3.4 ms decryption) and moderate memory consumption (120 KB). The network performance analysis proves that smaller block sizes (5 KB) provide 245 Tx/s throughput using 0.2 J energy, thus, being utilized in real-time IoT operation. PBFT protocol achieves high levels of latency (90 ms) and finality (1.1 s) reduction than the Proof-of-Work and Proof-of-Stake, and uses up to 96 percent less energy than PoW. Using homomorphic encryption with lightweight consensus is a good compromise between security, scalability, and achievable energy efficiency that fits well in next-generation IoT deployments where trust, privacy, and performance are of utmost importance.
D Abisha, Ashwanth Raj A, Sunil Kumar S, Senthil Kumar M J · 5 authors
The exponential growth of data driven applications in domains such as AI, healthcare, and IoT demands secure, transparent, and decentralized platforms for sharing high quality datasets. Traditional centralized systems suffer from opaque pricing, privacy risks, single points of failure, and lack of verifiable ownership, limiting trust and scalability. This paper proposes AquaStream, a decentralized data marketplace that leverages blockchain, the InterPlanetary File System (IPFS), and Ethereum smart contracts to tokenize datasets as non-fungible tokens (NFTs), ensuring verifiable ownership and tamper-proof traceability. The system employs dual-layer encryption (AES-256/RSA) to safeguard data during storage and transmission, while IPFS provides distributed storage to improve availability and reduce on-chain storage costs. Smart contracts based on ERC-20 and ERC-721 standards automate secure payments and access control, eliminating intermediaries and enhancing transparency. A React.js-based interface with MetaMask integration enables intuitive dataset management for both technical and non-technical users. Experimental evaluation on the Ethereum Sepolia testnet and Polygon Mumbai network demonstrates significant performance gains, including up to 73.6% reduction in gas costs using Layer 2 scaling and dataset retrieval latency as low as 4.2 seconds. These results confirm AquaStream’s efficiency, cost-effectiveness, and scalability. By bridging the gap between data providers and consumers, AquaStream fosters a collaborative ecosystem that promotes data ownership, privacy, and equitable monetization, offering a robust foundation for future decentralized data economies.
N.Shanmuga Priya, M. A. Gopinath, V Gunaseelan., K Guruganesh
This paper presents a new decentralized e-voting architecture designed to create tamper-proof, transparent, and secure election systems using the blockchain and smart contracts. The solution uses a permissioned blockchain structure that is private and Hyperledger Fabric-based in an effort to improve scalability and privacy while retaining the inherent features of decentralization. A zero-knowledge proof (ZKPs) based novel authentication method is implemented to provide eligibility checking and safeguard the privacy of the voter. The smart contracts are aimed at automating ballot tallying, publishing results, and checking their validity. The system is also resistant to traditional channels of attack such as denial-of-service, vote tampering, and voting redundancies based on its distributed ledger and consensus algorithms. A light-weight online interface has been implemented in an effort to promote usability and accessibility, thereby showing an unproblematic voter experience. Experimental results have now made it feasible to deploy the system in organizational and government voting applications, thereby testing its effectiveness within these settings. This method represents a tangible step towards an entirely reliable digital democracy.
Supply chain finance, a critical tool for industrial chain coordination in the digital economy, faces challenges such as centralized identity authentication, data silos, and privacy risks, with traditional models constrained by single-point failures and inefficient data sharing.While blockchain's decentralized and tamper-proof features offer a solution, existing approaches often lack comprehensiveness.To address this, this study proposes two integrated solutions: first, the ZK-SCFI scheme, which leverages Merkle trees for identity storage, Paillier homomorphic encryption for pseudo-identity generation, and zero-knowledge proofs for privacy-preserving verification, effectively avoiding centralized risks and ensuring transaction non-associability; second, the TRU-SABE framework, combining blockchain and IPFS to enhance ciphertext-policy attribute-based encryption (CP-ABE) with keyword search, user revocation, outsourced decryption, and malicious user tracking, addressing traditional limitations like high computational costs and scalability issues.Experiments demonstrate TRU-SABE's superior efficiency, with user-side decryption overhead reduced to 3TE and storage advantages from factor group structures for attribute keys and ciphertexts, while the hybrid architecture alleviates data silos and storage pressure.A prototype system built on Fisco Bcos consortium blockchain, Spring Boot backend, and Vue.js frontend validates core functionalities, including encrypted data sharing and smart contract-based traceability.This work provides a holistic privacy-preserving solution for supply chain finance, with future directions focusing on balancing privacy with regulatory compliance, optimizing multi-authority key management, and expanding system capabilities to enhance security and efficiency.
In the context of a growing shift towards DeFi, the challenge of user identity verification while maintaining privacy remains a daunting task. This paper proposes a model for identity verification based on Zero-Knowledge Proofs (ZKPs) tailored for distributed financial contexts. The model implemented uses cryptographic methods to verify identity claims while maintaining the confidentiality of the personal information, achieving a delicate equilibrium between privacy and financial compliance. The privacy-compliant framework increases security while satisfying legal compliance by removing the need to trust a single party and decreasing exposure of personal information and data. The proposed model improves privacy, verification speed, and fraud resistance compared to conventional and baseline systems.
Md Bokhtiar Al Zami, Md Raihan Uddin, Dinh C. Nguyen
Federated learning (FL) has gained popularity as a privacy-preserving method of training machine learning models on decentralized networks. However to ensure reliable operation of UAV-assisted FL systems, issues like as excessive energy consumption, communication inefficiencies, and security vulnerabilities must be solved. This paper proposes an innovative framework that integrates Digital Twin (DT) technology and Zero-Knowledge Federated Learning (zkFed) to tackle these challenges. UAVs act as mobile base stations, allowing scattered devices to train FL models locally and upload model updates for aggregation. By incorporating DT technology, our approach enables real-time system monitoring and predictive maintenance, improving UAV network efficiency. Additionally, Zero-Knowledge Proofs (ZKPs) strengthen security by allowing model verification without exposing sensitive data. To optimize energy efficiency and resource management, we introduce a dynamic allocation strategy that adjusts UAV flight paths, transmission power, and processing rates based on network conditions. Using block coordinate descent and convex optimization techniques, our method significantly reduces system energy consumption by up to 29.6% compared to conventional FL approaches. Simulation results demonstrate improved learning performance, security, and scalability, positioning this framework as a promising solution for next-generation UAV-based intelligent networks.
Zero-Knowledge Proofs (ZKP) are protocols which construct cryptographic proofs to demonstrate knowledge of a secret input in a computation without revealing any information about the secret. ZKPs enable novel applications in private and verifiable computing such as anonymized cryptocurrencies and blockchain scaling and have seen adoption in several real-world systems. Prior work has accelerated ZKPs on GPUs by leveraging the inherent parallelism in core computation kernels like Multi-Scalar Multiplication (MSM). However, we find that a systematic characterization of execution bottlenecks in ZKPs, as well as their scalability on modern GPU architectures, is missing in the literature. This paper presents ZKProphet, a comprehensive performance study of Zero-Knowledge Proofs on GPUs. Following massive speedups of MSM, we find that ZKPs are bottlenecked by kernels like Number-Theoretic Transform (NTT), as they account for up to 90% of the proof generation latency on GPUs when paired with optimized MSM implementations. Available NTT implementations under-utilize GPU compute resources and often do not employ architectural features like asynchronous compute and memory operations. We observe that the arithmetic operations underlying ZKPs execute exclusively on the GPU's 32-bit integer pipeline and exhibit limited instruction-level parallelism due to data dependencies. Their performance is thus limited by the available integer compute units. While one way to scale the performance of ZKPs is adding more compute units, we discuss how runtime parameter tuning for optimizations like precomputed inputs and alternative data representations can extract additional speedup. With this work, we provide the ZKP community a roadmap to scale performance on GPUs and construct definitive GPU-accelerated ZKPs for their application requirements and available hardware resources.
We quantify the economic consequences of Ethereum’s transition from Proof-of-Work to Proof-of-Stake. We document a structural break in inflation dynamics, shifting to an ARIMA(2,1,1) process with deflationary tendencies. The relationship between inflation and staking returns weakens post-Merge, challenging assumptions about incentive structures in Proof-of-Stake systems. Analysis reveals significant changes in market microstructure, including reduced spot trading volume and altered futures market behavior. We identify complex feedback loops between on-chain metrics and market variables, defying traditional equilibrium models. Our results suggest the need for new economic models to understand Proof-of-Stake systems and their market implications.
Purpose: to develop a methodological framework for selecting the optimal technology for building cross-border payment infrastructure based on the criterion of decentralization of key financial system actors. Methods: structural analysis of objects, a systems approach, a service approach, a method of structural-matrix analysis of concepts, a research method from general to specific, a comparative analysis method. Results: payment institutions and infrastructure are classified as the main factors influencing the qualitative and quantitative characteristics of cross-border payments. Such characteristics can be improved by forming a cross-border payment infrastructure based on distributed ledger technology, which allows for more equal relations between its users. The features of a cross-border payment infrastructure based on distributed ledger technology include mechanisms for forming, using, maintaining identity and protecting processes, objects and data, which provide it with the required functionality. A comparative analysis with centralized data processing systems shows the advantages of using distributed ledger technology to form a cross-border payment infrastructure. The signs of a payment's cross-border nature are determined by splitting the payment into fragments and identifying pairs of payment subjects located in different jurisdictions. It has been established that a number of cross-border payment subjects may be located outside the payment space and, under certain circumstances, fail to perform their functionality. Numerical indicators of the level of a cross-border payment dependence on the actions of entities outside the payment space are proposed. A model of a decentralized cross-border payment infrastructure is constructed, containing one structural level and an integrated payment token. Conclusions and Relevance: the proposed model can serve as a methodological foundation for the practical implementation of the task of developing cross-border payment infrastructure that ensures a sufficient level of key actors decentralization, meets the needs of economic agents in conducting cross-border payments, and possesses long-term development potential.
The increasing adoption of electric vehicles has spurred significant interest in Vehicle-to-Grid technology as a transformative approach to modern energy systems. This paper presents a systematic review of V2G systems, focusing on their integration challenges and potential solutions. First, the current state of V2G development is examined, highlighting its growing importance in mitigating peak demand, enhancing voltage and frequency regulation, and reinforcing grid resilience. The study underscores the pivotal role of artificial intelligence and machine learning in optimizing energy management, load forecasting, and real-time grid control. A critical analysis of cybersecurity risks reveals heightened vulnerabilities stemming from V2G's dependence on interconnected networks and real-time data exchange, prompting an exploration of advanced mitigation strategies, including federated learning, blockchain, and quantum-resistant cryptography. Furthermore, the paper reviews economic and market aspects, including business models (V2G as an aggregator or due to self-consumption), regulation (as flexibility service provider) and factors influencing user acceptance shaping V2G adoption. Data from global case studies and pilot programs offer a snapshot of how V2G has been implemented at different paces across regions. Finally, the study suggests a multi-layered framework that incorporates grid stability resilience, cybersecurity resiliency, and energy market dynamics and provides strategic recommendations to enable scalable, secure, and economically viable V2G deployment.
Blockchain has been promoted as a remedy for coordination in fragmented, multi-stakeholder ecosystems, yet many projects stall at pilot stage. Using a design-science approach, we develop the Hybrid Cooperative (HC), a digitally native governance architecture that combines smart-contract coordination with a minimal, code-deferent legal interface and jurisdictional modules. This selective decentralization decentralizes rules where programmability lowers agency and verification costs, and centralizes only what is needed for enforceability. A post-case evaluation against two traceability initiatives in supply chains illustrates how the HC improves distributed task management, verifiable information, incentive alignment, institutional interoperability, and scalable, contestable governance. The paper contributes to Information Systems by specifying a socio-technical model for scalable, multi-stakeholder coordination across regulatory and organizational boundaries.
Ştefan-Claudiu Susan, Andrei Arusoaie, Dorel Lucanu
The high rate of false alarms from static analysis tools and Large Language Models (LLMs) complicates vulnerability detection in Solidity Smart Contracts, demanding methods that can formally or empirically prove the presence of defects. This paper introduces a novel detection pipeline that integrates custom Slither-based detectors, LLMs, Kontrol, and Forge. Our approach is designed to reliably detect defects and generate proofs. We currently perform experiments with promising results for seven types of critical defects. We demonstrate the pipeline's efficacy by presenting our findings for three vulnerabilities -- Reentrancy, Complex Fallback, and Faulty Access Control Policies -- that are challenging for current verification solutions, which often generate false alarms or fail to detect them entirely. We highlight the potential of either symbolic or concrete execution in correctly classifying such code faults. By chaining these instruments, our method effectively validates true positives, significantly reducing the manual verification burden. Although we identify potential limitations, such as the inconsistency and the cost of LLMs, our findings establish a robust framework for combining heuristic analysis with formal verification to achieve more reliable and automated smart contract auditing.
Quorum systems are a common way to formalize failure assumptions in distributed systems. Traditionally, these assumptions are shared by all involved processes. More recently, systems have emerged which allow processes some freedom in choosing their own, subjective or asymmetric, failure assumptions. For such a system to work, individual processes' assumptions must be compatible. However, this leads to a Catch-22-style scenario: How can processes collaborate to agree on compatible failure assumptions when they have no compatible failure assumptions to start with? We introduce asymmetric grid quorum systems that allow a group of processes to specify heterogeneous trust assumptions independently of each other and without coordination. They are based on qualitative attributes describing how the processes differ. Each process may select a quorum system from this class that aligns best with its subjective view. The available choices are designed to be compatible by definition, thereby breaking the cycling dependency. Asymmetric grid quorum systems have many applications that range from cloud platforms to blockchain networks.
Cryptocurrencies have revolutionized traditional finance by providing decentralized payment methods and disrupting global solutions. However, the increasing prevalence of cryptocrime threatens financial market security and public confidence. This study, “The Ripple Effect,” examines the financial damage caused by crypto-attacks on global payment networks and the regulatory complexities arising from deceitful cryptocurrency activity. It also examines the economic impact of cryptocrime, affecting individuals, organizations, and countries. The chapter highlights the financial threats that span across global markets and the regulatory barriers governments and organizations face. The fight against crypto crime requires international cooperation, sophisticated legal frameworks, and innovative blockchain analytical tools. The study emphasizes the need for transparency initiatives, education programs, and robust communication methods to restore trust within the crypto community.
Tema je primjena kriptovaluta u turizmu i ugostiteljstvu, s naglaskom na stavove i iskustva turista. Kriptovalute se sve više spominju kao potencijalno sredstvo plaćanja koje može donijeti brojne prednosti poput bržih i jeftinijih transakcija, smanjenja troškova konverzije valuta i većeg stupnja sigurnosti. Unatoč tome, njihova je upotreba u praksi još uvijek ograničena, ponajviše zbog volatilnosti cijena, sigurnosnih rizika, nedostatka regulative i nedovoljne razine informiranosti korisnika. Predmet istraživanja odnosi se na motivaciju turista za korištenje kriptovaluta, prepreke koje ih u tome sprječavaju, kao i na procjenu utjecaja mogućnosti plaćanja digitalnim valutama na izbor destinacija, hotela i restorana. Posebna pažnja posvećuje se i procjeni dugoročne održivosti kriptovaluta u turističkoj ponudi. Istraživanje je provedeno metodom anketiranja na uzorku od 72 ispitanika, a podaci su obrađeni primjenom deskriptivne statistike. Rezultati su pokazali da većina ispitanika poznaje kriptovalute samo pov
The quickly advancing development of artificial intelligence (AI) and rapidly spreading cryptocurrencies established a modern environment for both legal developments and illegal practices. The analysis explores AI-related crime activities in cryptocurrencies through a study of AI technologies that assist and combat various crypto-based criminal operations. This paper investigates AI-driven crimes which include fraud alongside money laundering and cyberattacks and evaluates the dual capabilities of AI between criminal execution and prevention functions. This paper explains the ethical and legal risks of AI implementations in cryptocurrency crimes and provides recommendations for present and future research to tackle emerging threats.