Liang, Hanzhong, Yue Duan, Xing Su, Xiao Li · 8 authors
As the Web3 ecosystem evolves toward a multi-chain architecture, cross-chain bridges have become critical infrastructure for enabling interoperability between diverse blockchain networks. However, while connecting isolated blockchains, the lack of cross-chain transaction pairing records introduces significant challenges for security analysis like cross-chain fund tracing, advanced vulnerability detection, and transaction graph-based analysis. To address this gap, we introduce ConneX, an automated and general-purpose system designed to accurately identify corresponding transaction pairs across both ends of cross-chain bridges. Our system leverages Large Language Models (LLMs) to efficiently prune the semantic search space by identifying semantically plausible key information candidates within complex transaction records. Further, it deploys a novel examiner module that refines these candidates by validating them against transaction values, effectively addressing semantic ambiguities and identifying the correct semantics. Extensive evaluations on a dataset of about 500,000 transactions from five major bridge platforms demonstrate that ConneX achieves an average F1 score of 0.9746, surpassing baselines by at least 20.05\%, with good efficiency that reduces the semantic search space by several orders of magnitude (1e10 to less than 100). Moreover, its successful application in tracing illicit funds (including a cross-chain transfer worth $1 million) in real-world hacking incidents underscores its practical utility for enhancing cross-chain security and transparency.
Shashank Motepalli, N. Garg, Gengrui Zhang, Hans‐Arno Jacobsen
Geospatial decentralization is essential for blockchains, ensuring regulatory resilience, robustness, and fairness. We empirically analyze five major Proof of Stake (PoS) blockchains: Aptos, Avalanche, Ethereum, Solana, and Sui, revealing that a few geographic regions dominate consensus voting power, resulting in limited geospatial decentralization. To address this, we propose Geospatially aware Proof of Stake (GPoS), which integrates geospatial diversity with stake-based voting power. Experimental evaluation demonstrates an average 45% improvement in geospatial decentralization, as measured by the Gini coefficient of Eigenvector centrality, while incurring minimal performance overhead in BFT protocols, including HotStuff and CometBFT. These results demonstrate that GPoS can improve geospatial decentralization {while, in our experiments, incurring minimal overhead} to consensus performance.
This paper presents the first empirical analysis of how diverse token-based reward mechanisms impact platform dynamics and user behaviors. For this, we gather a unique, large-scale dataset from Farcaster. This blockchain-based, decentralized social network incorporates multiple incentive mechanisms spanning platform-native rewards, third-party token programs, and peer-to-peer tipping. Our dataset captures token transactions and social interactions from 574,829 wallet-linked users, representing 64.25% of the platform's user base. Our socioeconomic analyses reveal how different tokenomics design shape varying participation rates (7.6%--70%) and wealth concentration patterns (Gini 0.72--0.94), whereas inter-community tipping is 1.3--2x more frequent among non-following pairs, thereby mitigating echo chambers. Our causal analyses further uncover several critical trade-offs: (1) while most token rewards boost content creation, they often fail to enhance -- sometimes undermining -- content quality; (2) token rewards increase follower acquisition but show neutral or negative effects on outbound following, suggesting potential asymmetric network growth; (3) repeated algorithmic rewards demonstrate strong cumulative effects that may encourage strategic optimization. Our findings advance understanding of cryptocurrency integration in social platforms and highlight challenges in aligning economic incentives with authentic social value.
Qi Xia, Hu Xia, Isaac Amankona Obiri, Adjei-Arthur Bonsu · 11 authors
Blockchain technology facilitates the development of decentralized systems that ensure trust and transparency without the need for expensive centralized intermediaries. However, existing blockchain architectures particularly consortium blockchains face critical challenges related to scalability and efficiency. State sharding has emerged as a promising approach to enhance blockchain scalability and performance. However, current shard-based solutions often struggle to guarantee fair participation and a balanced workload distribution among consortium members. To address these limitations, we propose Trusted Intelligent NetChain (TINC), a multi-plane sharding architecture specifically designed for consortium blockchains. TINC incorporates intelligent mechanisms for adaptive node assignment and dynamic workload balancing, enabling the system to respond effectively to changing network conditions while maintaining equitable shard utilization. By decoupling the control and data planes, TINC allows control nodes to focus on consensus operations, while data nodes handle large-scale storage, thus improving overall resource efficiency. Extensive experimental evaluation and formal analysis demonstrate that TINC significantly outperforms existing shard-based blockchain frameworks. It achieves higher throughput, lower latency, balanced node and transaction distributions, and reduced transaction failure rates. Furthermore, TINC maintains essential blockchain security guarantees, exhibiting resilience against Byzantine faults and dynamic network environments. The integration of Dynamic Decentralized Identifiers (DDIDs) further strengthens trust and security management within the consortium network.
Intrusion Detection Systems (IDS) play a critical role in protecting modern networks, but traditional centralized designs raise serious concerns regarding data privacy, trust, and scalability. Federated Learning (FL) reduces privacy risks through decentralized model training, and blockchain enhances trust by providing immutability and transparency. Combining these technologies creates a promising paradigm for secure and trustworthy IDS. This paper presents a comprehensive survey of blockchain-federated IDS with a particular focus on privacy and trust. The key contribution is a multi-dimensional taxonomy that integrates IDS architectures, FL strategies, blockchain types, and consensus mechanisms, providing a clear and structured view of this emerging field. We categorize threats into data, communication, and model levels, and map representative defense mechanisms to each. We also review applications in vehicular networks, industrial and medical Internet of Things (IoT), and metaverse scenarios. Finally, we highlight key challenges, including non-IID data, lightweight consensus, incentive mechanisms, and poisoning-resilient aggregation, and outline future research directions.
The article explores the potential of blockchain technology and smart contracts in the field of public administration. The emphasis is on the legal challenges that arise in the process of implementing relevant innovations, as well as on the opportunities they open up for increasing transparency, efficiency, and trust in state institutions. The relevance of the topic is due to global digitalization processes, the need to modernize public administration, and the growing demand from society for openness and public control over the work of state authorities. The novelty of the study lies in the study of the legal aspect of integrating decentralized technologies into the public sphere, which has not yet been sufficiently developed in the Ukrainian legal community. The international experience of regulating smart contracts is analyzed, legal gaps in Ukrainian legislation are identified, and proposals for its improvement are formulated. The results obtained may be useful for legislators, representatives of state bodies, and researchers in the field of digitalization of processes in public administration. Furthermore, the research highlights practical applications of blockchain and smart contracts in various public administration sectors, including digital identity management, tax collection, social welfare distribution, and property registration. By examining pilot projects and international case studies, the study demonstrates how these technologies can streamline administrative processes, reduce bureaucracy, and minimize the risk of corruption. The findings suggest that a gradual, regulated integration of blockchain solutions could significantly enhance operational efficiency and citizen satisfaction. Finally, the study addresses the potential risks and limitations associated with blockchain adoption in the public sector, including high implementation costs, technological challenges, and legal uncertainty. It emphasizes the importance of developing comprehensive regulatory frameworks, establishing clear standards for smart contract usage, and ensuring that public sector employees are equipped with the necessary technical skills. The paper concludes that while blockchain offers transformative opportunities, its successful adoption in public administration depends on a balanced approach that combines technological innovation with legal and institutional preparedness.
Open access
Legal, Health, Environmental and COVID-19 Challenges
The increasing complexity of urban energy systems requires decentralized, sustainable, and scalable solutions. The paper presents a new multi-layered framework for smart energy management in microgrids by bringing together advanced forecasting, decentralized decision-making, evolutionary optimization and blockchain-based coordination. Unlike previous research addressing these components separately, the proposed architecture combines five interdependent layers that include forecasting, decision-making, optimization, sustainability modeling, and blockchain implementation. A key innovation is the use of Temporal Fusion Transformer (TFT) for interpretable multi-horizon forecasting of energy demand, renewable generation, and electric vehicle (EV) availability which outperforms conventional LSTM, GRU and RNN models. Another novelty is the hybridization of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), to simultaneously support discrete and continuous decision variables, allowing for dynamic pricing, efficient energy dispatching and adaptive EV scheduling. Multi-Agent Reinforcement Learning (MARL) which is improved by sustainability shaping by including carbon intensity, renewable utilization ratio, peak to average load ratio and net present value in agent rewards. Finally, Ethereum-based smart contracts add another unique contribution by providing the implementation of transparent and tamper-proof peer-to-peer energy trading and automated sustainability incentives. The proposed framework strengthens resilient infrastructure through decentralized coordination and intelligent optimization while contributing to climate mitigation by reducing carbon intensity and enhancing renewable integration. Experimental results demonstrate that the proposed framework achieves a 14.6% reduction in carbon intensity, a 12.3% increase in renewable utilization ratio, and a 9.7% improvement in peak-to-average load ratio compared with baseline models. The TFT-based forecasting model achieves RMSE = 0.041 kWh and MAE = 0.032 kWh, outperforming LSTM and GRU by 11% and 8%, respectively.
In the evolving landscape of cybercrime and digital investigations, the integrity and traceability of digital evidence are paramount.Traditional chain-of-custody (CoC) mechanisms in digital forensics rely heavily on centralized systems, manual logging, and institutional trust, all of which are prone to human error, tampering, and data loss.This study introduces a blockchain-based chain-of-custody model aimed at ensuring immutable, transparent, and verifiable tracking of digital evidence across its entire lifecycle-from acquisition and analysis to presentation in court.Leveraging blockchain's decentralized architecture and cryptographic immutability, the proposed framework records every interaction with digital evidence, including transfers, access logs, and analysis events, in a tamper-proof ledger distributed across trusted nodes in a forensic network.Smart contracts automate procedural compliance, access permissions, and time-stamping, thereby reducing reliance on third-party oversight and enhancing procedural integrity.The system was designed and simulated using Hyperledger Fabric, integrating role-based access control and hash-based evidence fingerprinting.Benchmark testing demonstrated the model's robustness in preserving forensic timelines under various adversarial scenarios, including internal breaches and unauthorized access attempts.In addition to enhancing evidentiary credibility, this blockchain-enhanced CoC model offers interoperability with existing digital forensic tools and forensic readiness systems.By aligning with legal admissibility standards and ensuring end-to-end accountability, the framework strengthens the evidentiary chain, particularly in multi-jurisdictional and cloudbased investigations.As digital forensics faces increasing scrutiny regarding evidentiary handling, this research presents a scalable and future-proof alternative to conventional CoC practices-crucial for maintaining the rule of law in cybercrime prosecutions.
The increasing complexity and sophistication of financial fraud have necessitated more effective and real-time solutions for monitoring, detecting, and preventing illicit activities in the financial sector. Blockchain technology, with its inherent features of decentralization, immutability, and transparency, has emerged as a promising tool to address these challenges, particularly when integrated with Regulatory Technology (RegTech) systems. This explores the potential of blockchain-powered RegTech solutions for enhancing fraud detection and supporting legal oversight in financial institutions. Blockchain’s decentralized ledger system provides a secure and transparent environment where financial transactions can be monitored in real time. The integration of machine learning algorithms with blockchain analytics allows for the identification of suspicious patterns and anomalies, enabling rapid detection of fraudulent activities. Additionally, blockchain facilitates the automation of compliance reporting, reducing operational costs and ensuring regulatory standards are met with minimal human intervention. The use of smart contracts further streamlines the enforcement of compliance rules, providing a seamless and tamper-proof audit trail. Furthermore, blockchain has the potential to harmonize international compliance standards, enabling more efficient cross-border regulatory enforcement. Through its use in decentralized identity verification and AML (Anti-Money Laundering) systems, blockchain can enhance the traceability and transparency of financial transactions, addressing the challenges of jurisdictional fragmentation and inconsistent regulations across countries. Privacy-preserving technologies, such as zero-knowledge proofs, also ensure that data protection laws like GDPR are respected while maintaining regulatory oversight. This concludes by highlighting the substantial benefits blockchain-powered RegTech systems offer for real-time fraud detection and regulatory compliance, urging financial institutions and regulators to collaborate on adopting these technologies to safeguard the integrity of global financial systems. Keywords: Harnessing, Blockchain-powered, RegTech systems, Real-Time, Fraud Detection, Legal Oversight, Financial Institutions.
ABSTRACT To address the core challenges of information asymmetry, privacy leakage, and low storage efficiency in rice supply chains, this study proposes an enhanced traceability system that integrates blockchain, adaptive encryption, and lightweight zero‐knowledge proofs. The system features a dynamic role‐based encryption model, where encryption levels are determined by both data sensitivity and role‐based weights. This model was designed and validated through surveys involving 50 stakeholders. By adopting an on‐chain and off‐chain collaborative storage architecture that leverages Merkle trees and IPFS, the system achieves a 67% reduction in storage overhead. Furthermore, an optimized Groth16‐based ZKP protocol ensures rapid verification in under 180 ms on ARM‐based devices. Experimental results demonstrate that, at a scale of 100,000 records, the system attains a transaction processing capacity of 328 TPS and an information entropy of 3.87, representing a 51% improvement over single‐layer encryption schemes. The monthly deployment cost remains affordable for smallholder farmers, ranging from $2 to $5. The system also supports interoperability with external traceability frameworks through cross‐chain channels and adaptation to the GS1 EPCIS standard, facilitating trusted collaboration in transnational rice supply chains. By effectively balancing data integrity and privacy protection, this solution significantly enhances system scalability and offers a novel pathway for the digital transformation of agricultural supply chains.
John Adeyemi O, Folasade Yetunde Ayankoya, Kuyoro S. O
The advancement of technology has positioned blockchain and machine learning (ML) as transformative forces in finance. Blockchain’s decentralized structure ensures secure and transparent transactions, while ML processes vast data to identify patterns and enhance decision-making. Their integration offers significant potential for fraud detection, risk assessment, and transaction optimization. Blockchain provides a tamper-proof environment, ensuring data integrity and reducing fraud. Meanwhile, ML detects anomalies, predicts market trends, and automates processes, improving financial security and efficiency. However, challenges such as scalability, computational demands, and data privacy hinder widespread adoption. Blockchain struggles with high costs and limited throughput, while ML requires significant resources and quality data. Emerging solutions like federated learning for privacy-preserving ML, zero-knowledge proofs for secure transactions, and hybrid blockchain models for scalability aim to address these challenges. Overcoming these barriers will enable a more secure, efficient, and data-driven financial ecosystem.
As cloud adoption grows, Identity and Access Management (IAM) faces increasing complexity due to reliance on centralized systems controlled by Cloud Service Providers (CSPs), raising concerns over data leakage and single points of failure. This paper proposes BZET-IAM, a trust-based Single Sign-On IAM (SSO-IAM) framework built on Consortium Blockchain to support hybrid, multi-application cloud environments across domains. The system integrates Zero Trust Architecture (ZTA), Self-Sovereign Identity (SSI), and Zero-Knowledge Proofs (ZKPs) to enable dynamic, privacy-preserving authentication using verifiable claims. Trust-scores and contextual information are embedded in access tokens to support continuous verification and adaptive access control. Blockchain ensures tamper-proof, auditable authentication and authorization. Experimental results show that the proposed approach achieves lower authentication and privilege update costs under dynamic, context-aware access scenarios.
Mohammed Abdelhamid Nedioui, Ali Khechekhouche, Konstantinos Κarampidis, Giorgos Papadourakis · 5 authors
The integration of artificial intelligence (AI) and blockchain technology opens new avenues for decentralized, transparent, and secure data-driven systems. However, ensuring privacy and verifiability in collaborative AI environments remains a key challenge, especially when model updates or decisions must be recorded immutably on-chain. In this paper, we propose a novel privacy-preserving framework that leverages an ElGamal-based aggregate signature scheme with aggregate public keys to enable secure, verifiable, and unlinkable multi-party contributions in blockchain-based AI ecosystems. This approach allows multiple AI agents or data providers to jointly sign model updates or decisions, producing a single compact signature that can be publicly verified without revealing the identities or individual public keys of contributors. The design is particularly well-suited to resource-constrained or privacy-sensitive applications such as federated learning in healthcare or finance. We analyze the security of the scheme under standard assumptions and evaluate its efficiency in different terms. The study and experimental results demonstrate the potential of our framework to enhance trust and privacy in AI collaborations over decentralized networks.
Christiana Chamon, Leyla Nazhandali, Vinod Lohani, Dayoung Kim
This Innovative Practice Work-in-Progress Paper presents an ongoing effort to develop a blockchain-based micro-credentialing system for engineering education, addressing the challenges of capturing and verifying granular skill achievements in digital learning environments. By leveraging blockchain technology, privacy-preserving biometric authentication, and incentive mechanisms like Proof of Stake Learning (PoSL), our system ensures secure, tamper-proof, and portable micro-credentials. Integrated into Virginia Tech's ECE 2564: Introduction to Embedded Systems course, the platform allows students to earn NFT Knowledge Coins for validated contributions, preparing them for workforce entry through coding interview simulations. Preliminary findings indicate strong student interest in self-sovereign credentialing and faculty support for automated assessments. This work aims to enhance transparency, equity, and employer trust in micro-credentials, with broader impacts on underrepresented groups and regional STEM education.
Academic integrity and plagiarism
Information Systems Education and Curriculum Development
Jaeheon Kwak, Jaeuk Lee, Hyoji Ha, Haewon Kim · 6 authors
We present a visual analytics system to increase proposal approval likelihood in Proof-of-Stake(PoS) blockchain governance. The proposed system introduces the Contextual Alliance Index(CAI), a similarity metric reflecting contextual information based on proposal voting data. Through heatmaps and bubble heap graphs, users can explore alliances among validators and support comparison and prioritization of persuadable validators. Furthermore, a case study illustrates an analytical process for understanding voting patterns for each proposal, and for identifying alliances and the prioritization of persuadable validators when drafting new proposals. This study is expected to contribute to the in-depth analysis of proposal patterns and the development of effective proposal strategies by identifying validator alliances in PoS blockchain governance.