Similar to all other cryptocurrency platforms, Ethereum is constantly confronted with malicious activities. In recent years, research efforts have targeted the detection and mitigation of malicious activities and the associated accounts within the Ethereum ecosystem. Yet, the malicious accounts represent only a small visible part of the substantial collaborative network enabling these activities. In this work, we offer the first analysis of this collaborative network and the corresponding affiliate accounts that often remain hidden from detection. We present enEtherShield, an enhanced framework for detecting affiliate accounts that assist malicious accounts in the related Ethereum scams. Our research findings lay the foundation for the detection of the collaborative network enabling Ethereum scams.
Ádám Bereczk, Zoltán Musinszki, Erika Szilágyiné Fülöp, Bettina Hódiné Hernádi
This study investigates the allocation of pre-sale capital by blockchain technology-based startup ventures, with a specific focus on the Play-to-Earn (P2E) segment within the Web3 ecosystem, and its impact on token price performance. Our aim is to determine the proportion of initial capital that P2E startups, according to their business plan (whitepaper), allocated to key areas such as team and advisor expenses, marketing activities, and product development. Subsequently, this research centers on the question of how the focal areas of pre-sale capital utilization (team, marketing, development) correlate with the subsequent price performance of the tokens issued by these startups. The timeliness and relevance of this topic are underscored by the dynamic evolution of blockchain technology and the P2E model, as well as the critical role of startups' capital allocation decisions. Understanding how the utilization of initial funding influences long-term value is also of paramount importance for investors. Based on the results, while excessive marketing expenditures may offer a project short-term benefits, this strategy can potentially have negative long-term consequences. A project's financial viability is contingent upon competent human resources and the insights of external experts; nevertheless, these elements alone are not definitively sufficient. The significance of product development was only evident when the effect was measured in Bitcoin terms; no correlation was found when measured in Dollars.
This work titled "RFID Based Campuswide Payment System" introduces an innovative cashless payment solution for educational institutions. It uses RFID cards and a Raspberry Pi to enable hassle free payments for various campus services, such as cafeteria purchases, tuition fees, and library fines. A centralized database ensures real-time updates on transactions and account balances, accessible through a simple and user-friendly web interface. This work is involved in designing a secure system with object-oriented principles, setting up databases, and integrating hardware like RFID readers with a Raspberry Pi. The systems are proved to be a cost-effective and efficient alternative to traditional payment methods, enhancing convenience and security for students and administrators. The study also explored similar RFID applications, like smart parking and attendance systems, to identify challenges and improvements. Looking ahead, it envisions features like wearable RFID devices, voice-activated payments, and blockchain integration to boost security and usability. Results show that this system simplifies campus payments and has the potential for broader adoption in similar environments.
Mohaimin Al Barat, Hexuan Yu, Shaoyu Li, Yang Xiao · 8 authors
Dynamic Spectrum Sharing (DSS) is a cornerstone of next-generation wireless systems, yet existing solutions such as Spectrum Access Systems (SAS) rely on centralized administrators that expose sensitive operational metadata and lack cryptographic transaction accountability. Though SAS administrators, such as Google, have introduced pay-as-you-go pricing models, these approaches still face significant privacy and accountability challenges as DSS evolves toward a more open and large-scale spectrum marketplace. We present SpexPay, a privacy-preserving and auditable pay-as-you-go spectrum usage framework that enforces fine-grained, usage-linked payments without revealing user identities. Spexpay integrates BBS+ verifiable credentials, unlinkable session pseudonyms, and selective-disclosure proofs to enforce privacy-preserving access authorization, while leveraging Solidity-based smart contracts to realize automated and non-repudiable escrow settlement. By recording only pseudonymous usage evidence and hash-chained metering data on-chain, the system achieves strong unlinkability while preserving verifiable accountability and auditability. A full prototype demonstrates low end-to-end latency ($\approx$150 ms) and modest on-chain cost ($\approx$603K gas or $\approx$\$0.9), showing that SpexPay is practical for real-world DSS deployments. We also evaluated the user-side cryptographic operations on a Raspberry Pi 5 to assess scalability and suitability for edge-class hardware. Our code and artifacts are publicly available at https://github.com/iambarat/SpexPay.
Legacy enterprise resource planning (ERP) systems serve as the operational backbone of global commerce but often create bottlenecks due to their rigid, monolithic design. As organizations incorporate artificial intelligence (AI), these outdated systems struggle to support high-speed, parallel workflows, creating a significant integration challenge. This paper introduces a non intrusive modernization approach that overlays a decentralized multi-agent system (MAS) onto existing infrastructure without requiring invasive code changes. By developing a digital twin of the order-to-cash (O2C) process, we train autonomous agents through multi-agent reinforcement learning (MARL) to manage credit validation, inventory allocation, and fulfillment. We adapt the centralized training, decentralized execution (CTDE) framework to meet O2C constraints, enabling agents to learn globally optimal strategies while operating independently. Simulation results show that this architecture surpasses rule-based robotic process automation (RPA) baselines, increasing total throughput by 6.9% over a monolithic setup, though at a 6.3% error rate due to aggressive allocation policies. These results indicate that decentralized agent-based orchestration provides a scalable approach for modernizing legacy ERPs, offering increased agility without the risks associated with platform replacement.
This record contains the presentation materials prepared for the Data for Policy July Fireside Chat, “Digital Infrastructures of Democracy,” delivered online on 20 July 2026 by Professor Igor Calzada and chaired by Professor Marta Poblet. The talk presents Calzada’s action-research programme on digital citizenship and the democratic governance of digital infrastructures. Building upon the Oxford Research Encyclopedia article Digital Infrastructures of Democracy, it conceptualises democracy as being increasingly mediated by three interconnected infrastructural layers: material infrastructure, data infrastructure and algorithmic infrastructure. These layers are not politically neutral; their ownership, design and governance shape participation, rights, public accountability and the distribution of power. The presentation connects this conceptual framework with research on AI economics, Web3 decentralisation, the Digital Metropolis, EcoTechnoPolitics, territorial digital inclusion, data cooperatives and anticipatory AI governance. Particular attention is devoted to evidence from the Basque Country and Gipuzkoa, including the emerging supercomputing and quantum ecosystem associated with IBM Quantum System Two. The Basque Country is examined not as a model to be replicated mechanically, but as a city-regional laboratory whose lessons can support context-sensitive institutional learning elsewhere. The central argument is that democratic resilience requires the alignment of technical design, institutional reform and civic agency. Advanced computational capacity becomes a democratic public capability only when institutions can govern technological dependencies, territorialise benefits, ensure accountability, respect ecological limits and preserve meaningful opportunities for public participation and contestation.
Aditya Saraf, Ioannis Kaklamanis, Sarisht Wadhwa, Fatima Elsheimy
Censorship resistance is the defining advantage of blockchains over their centralized counterparts. Yet block proposers censor transactions for many reasons, from legal consequences to economic incentives. We study economically-incentivized censorship, modeled by an adversary who bribes proposers to exclude a target transaction, and define the economic censorship resistance (eCR) of a transaction as the adversary's expected cost of successful censorship divided by the user's expected payment for inclusion. Single-proposer systems are structurally weak by this measure: under a first-price auction the adversary need only match the user's bid, and fee burning pushes eCR to a few percent of what the user pays. We therefore turn to multiple concurrent proposers (MCP), where block capacity is divided among $n$ proposers and the block is the union of their sub-blocks. While MCP can substantially increase the cost of censorship by requiring the adversary to bribe many proposers, it also introduces transaction duplication, reducing throughput. The resulting trade-off depends critically on the transaction fee mechanism (TFM), which determines how fees are shared among competing proposers. We create a game theoretic model where validators construct blocks from a shared mempool, subject to an adversary's bribery attempt. We provide an algorithm that solves for the mixed equilibrium of a given mempool, which is characterized by the probability of including each transaction. This algorithm works for a wide class of TFMs, and allows us to calculate the expected throughput and censorship resistance for any bid distribution. We then use simulations to show how the eCR and throughput vary as the number of proposers increases. We compare three TFMs, finding that the duplication-penalizing TFM dominates the others across many settings. We also validate our findings with empirical Ethereum data.
Tahrim Hossain, Faisal Haque Bappy, Tarannum Shaila Zaman, Tariqul Islam
Blockchain platforms have grown into an ecosystem of independent networks, and a growing class of applications now requires smart contracts on separate chains to act as one. Such operations must be atomic, yet immutability makes this fundamentally harder: a confirmed transaction cannot be reversed, so the rollback on which classical atomic commitment protocols depend is unavailable. Two challenges follow. Contract state must be held across an operation whose outcome is not yet known, and each chain's execution outcome must be established even though no chain can observe another. In response, we introduce a framework that achieves atomicity through forward-only correction, resolving incomplete operations with new on-chain transactions rather than reversal. The framework bounds how long contract state is held and confines contention to the state an operation touches, and it establishes outcomes from an on-chain record of what each chain executed, without relying on any single coordinating party. This work lays the foundation for atomic coordination of general smart contract operations across heterogeneous blockchains.