Wan Ki Wong, Sahel Torkamani, Michele Ciampi, Rik Sarkar
Evaluating the usefulness of data before purchase is essential when obtaining data for high-quality machine learning models, yet both model builders and data providers are often unwilling to reveal their proprietary assets. We present PrivaDE, a privacy-preserving protocol that allows a model owner and a data owner to jointly compute a utility score for a candidate dataset without fully exposing model parameters, raw features, or labels. PrivaDE provides strong security against malicious behavior and can be integrated into blockchain-based marketplaces, where smart contracts enforce fair execution and payment. To make the protocol practical, we propose optimizations to enable efficient secure model inference, and a model-agnostic scoring method that uses only a small, representative subset of the data while still reflecting its impact on downstream training. Evaluation shows that PrivaDE performs data evaluation effectively, achieving online runtimes within 15 minutes even for models with millions of parameters. Our work lays the foundation for fair and automated data marketplaces in decentralized machine learning ecosystems.
Jiri Gavenda, Petr Svenda, Stanislav Bobon, Vladimir Sedlacek
A coinjoin protocol aims to increase transactional privacy for Bitcoin and Bitcoin-like blockchains via collaborative transactions, by violating assumptions behind common analysis heuristics. Estimating the resulting privacy gain is a crucial yet unsolved problem due to a range of influencing factors and large computational complexity. We adapt the BlockSci on-chain analysis software to coinjoin transactions, demonstrating a significant (10-50%) average post-mix anonymity set size decrease for all three major designs with a central coordinator: Whirlpool, Wasabi 1.x, and Wasabi 2.x. The decrease is highest during the first day and negligible after one year from a coinjoin creation. Moreover, we design a precise, parallelizable privacy estimation method, which takes into account coinjoin fees, implementation-specific limitations and users' post-mix behavior. We evaluate our method in detail on a set of emulated and real-world Wasabi 2.x coinjoins and extrapolate to its largest real-world coinjoins with hundreds of inputs and outputs. We conclude that despite the users' undesirable post-mix behavior, correctly attributing the coins to their owners is still very difficult, even with our improved analysis algorithm.
Chong Chen, Ze Liu, Lingfeng Bao, Yanlin Wang · 7 authors
The cryptocurrency market offers significant investment opportunities but faces challenges including high volatility and fragmented information. Data integration and analysis are essential for informed investment decisions. Currently, investors use three main approaches: (1) Manual analysis across various sources, which depends heavily on individual experience and is time-consuming and prone to bias; (2) Data aggregation platforms-limited in functionality and depth of analysis; (3) Large language model agents-based on static pretrained models, lacking real-time data integration and multi-step reasoning capabilities. To address these limitations, we present Coinvisor, a reinforcement learning-based chatbot that provides comprehensive analytical support for cryptocurrency investment through a multi-agent framework. Coinvisor integrates diverse analytical capabilities through specialized tools. Its key innovation is a reinforcement learning-based tool selection mechanism that enables multi-step planning and flexible integration of diverse data sources. This design supports real-time interaction and adaptive analysis of dynamic content, delivering accurate and actionable investment insights. We evaluated Coinvisor through automated benchmarks on tool calling accuracy and user studies with 20 cryptocurrency investors using our interface. Results show that Coinvisor improves recall by 40.7% and F1 score by 26.6% over the base model in tool orchestration. User studies show high satisfaction (4.64/5), with participants preferring Coinvisor to both general LLMs and existing crypto platforms (4.62/5).
Smart contracts play a significant role in automating blockchain services. Nevertheless, vulnerabilities in smart contracts pose serious threats to blockchain security. Currently, traditional detection methods primarily rely on static analysis and formal verification, which can result in high false-positive rates and poor scalability. Large Language Models (LLMs) have recently made significant progress in smart contract vulnerability detection. However, they still face challenges such as high inference costs and substantial computational overhead. In this paper, we propose ParaVul, a parallel LLM and retrieval-augmented framework to improve the reliability and accuracy of smart contract vulnerability detection. Specifically, we first develop Sparse Low-Rank Adaptation (SLoRA) for LLM fine-tuning. SLoRA introduces sparsification by incorporating a sparse matrix into quantized LoRA-based LLMs, thereby reducing computational overhead and resource requirements while enhancing their ability to understand vulnerability-related issues. We then construct a vulnerability contract dataset and develop a hybrid Retrieval-Augmented Generation (RAG) system that integrates dense retrieval with Best Matching 25 (BM25), assisting in verifying the results generated by the LLM. Furthermore, we propose a meta-learning model to fuse the outputs of the RAG system and the LLM, thereby generating the final detection results. After completing vulnerability detection, we design chain-of-thought prompts to guide LLMs to generate comprehensive vulnerability detection reports. Simulation results demonstrate the superiority of ParaVul, especially in terms of F1 scores, achieving 0.9398 for single-label detection and 0.9330 for multi-label detection.
Globally, the private sector is moving towards expanding the growth of tokenized assets trading, using private money channels (stablecoins) as payment systems. At the same time, the public sector, often central banks in alliance with private sector partners, is engaged in ambitious projects, exploring the feasibility of public payment systems using wholesale CBDCs to facilitate the cross-border use of fiat currencies and to ensure universal public trust in global cross-border payments. The advent of the use of distributed ledger technology over the past decade is enabling these activities to take place. This paper outlines these developments and their impacts and the future emergence of a global assets trading and payments ecosystem. The emergence of this system is also considered in the context of the gradual restructuring of global trade and monetary patterns, via private and public sector initiatives, and the potential significant impact of a Trumpian world order. The future seems to imply a more protectionist and, effectively, isolationist US—in monetary as well as trading terms.
This paper examines the transformation of decentralized financial ecosystems due to the emerging integration of artificial intelligence (AI) with cryptocurrency technologies. By enabling digital transactions that are adaptive, autonomous, and secure, AI-enhanced cryptocurrencies have the potential to upend conventional financial systems. While highlighting AI's potential to improve scalability, efficiency, fraud detection, and investment strategies within blockchain-based economies, this paper analyzes future trends, technical challenges, ethical concerns, and regulatory frameworks.
This paper introduces The Visual Ledger, a diagnostic analysis of European art history between 1849 and 1991 that treats painting as an archival instrument registering the progressive evacuation of ontological weight from the human figure. Using a diagnostic history methodology, the article argues that painters detected and documented shifts in how presence, consequence, and embodiment are organised decades before these transformations consolidated institutionally. Beginning with the material density of Realism in Courbet and Repin, the analysis traces a directional transformation through Impressionism’s optical dissolution, Pointillism’s cognitive fragmentation, Art Nouveau’s decorative masking, Malevich’s honest void, and Pop Art’s operationalisation of abstraction as governance. The AIDS crisis functions as a late twentieth-century visibility stress test, exposing the limits of surface-based legibility when confronted with suffering resistant to procedural categorisation. The paper demonstrates that visual archives independently converge with literary and administrative records, showing that populations currently collapsing under procedural abstraction are experiencing the settlement of a transformation painters registered when evacuation began. This contribution is conceptual and diagnostic rather than empirical. It forms one component of the broader Equilibrium Ledger framework, which examines how institutions generate and distribute cognitive costs, and how procedural abstraction renders embodied complexity administratively legible while humanly inaccessible.
In the digital era, managing royalties for creative works remains a major challenge. Existing systems are often outdated and lack the transparency and efficiency required to meet the growing demands of digital content distribution. This paper presents BlockRoyalty, an application based on blockchain and smart contract technologies, designed to modernize royalty management for digital books. The system automates the registration of authors, publishers and buyers. It manages book publication, applies dynamic pricing based on sales trends and ensures real-time royalty distribution. All transactions are securely and recorded in a verifiable manner on the blockchain, fostering trust among stakeholders.
This study investigates prospective Arab customers’ intentions to use cryptocurrencies. Using a quantitative approach, cross-sectional data from a purposive sample of 437 respondents were collected. The survey was distributed via 13 well-known social media platforms and Arab-focused social media groups. Direct, mediating, and moderating hypotheses are tested using structural equation modeling (SEM). The findings confirmed that the association between Digital Techno-stress (DTS) and the Intention to Adopt Cryptocurrency (IACR) is moderated by Ethical Issues (EI). Nevertheless, the study found that government regulations (GR) had no moderating effect on Arab cryptocurrency investors. The findings emphasize the necessity of ethical frameworks to increase credibility in Arab cryptocurrency marketplaces by fostering user-centric trading platforms, lowering techno-stress, and fostering trust.
Alexandru Ursu, Petru Lucian Curșeu, Sabina Trif, Alina Maria Fleştea
Cryptocurrencies are rapidly transforming digital finance and entrepreneurship, yet their adoption by entrepreneurs remains rather poorly understood. Drawing on the Threat-Rigidity Model (TRM) and the opportunity recognition literature, this study examines how entrepreneurial experience, financial literacy, perceived opportunities, and perceived threats influence entrepreneurial intention to use cryptocurrencies. We tested a moderated mediation model in which the association between financial literacy and experience, on the one hand, and intention to use cryptocurrencies, on the other, was mediated by perceived opportunities. In this model, perceived threats served as a moderator on the relationship between financial literacy and intention, as well as between perceived opportunities and adoption intention. Data were collected from a sample of 133 Romanian entrepreneurs across diverse industries. The results supported the mediating role of perceived opportunities in the relationship between financial literacy and intention to use cryptocurrencies in business and showed that the positive association between financial literacy and intention was attenuated by perceived threats. Entrepreneurial experience did not significantly influence perceived opportunities, while women entrepreneurs reported lower intention to adopt cryptocurrencies in business. This study is among the first to use the TRM to explore how the interplay of perceived opportunities and threats shapes cryptocurrency adoption in entrepreneurship. Other implications, limitations, and directions for future research are also discussed.
Iván Abellán Álvarez, Pol Hölzmer, Johannes Sedlmeir
Digital identity wallets promise significant advancements in digital identity management by offering users a high degree of convenience, security, and control over their data disclosure. However, there is also criticism regarding their privacy guarantees, especially when used in regulated use cases that require high levels of assurance on the correctness and binding of a legal identity. In this paper, we present a comprehensive privacy model and analysis of one of the most prominent digital wallets – the European Digital Identity Wallet (EUDIW) – as specified by the Architecture and Reference Framework (ARF) and the eIDAS 2.0 regulation. We employ a suite of qualitative privacy risk assessment methods to systematically map and evaluate information flows in three key use cases. Our analysis identifies multiple privacy risks – including linkability, identifiability, and excessive attribute data disclosure – and reveals that although the ARF is designed to comply with privacy-by-design principles, inherent design choices, such as the reliance on SD-JWT and mDOC data formats, as well as the concept of a Wallet Unit Attestation (WUA), retain risks to user privacy. Building on our findings, we then highlight how advanced Privacy-Enhancing Technologies (PETs), such as (general-purpose) Zero-Knowledge Proofs (ZKPs), can reduce or mitigate some of these risks.
Venture capital investment and hedge fund investment are two asset classes of alternative investment fund portfolios. The purpose of this study was to determine whether the digital currency named bitcoin truly adds to diversification in an alternative investment fund portfolio. Vector auto regression was used to determine any unidirectional or bidirectional relationship between variables. The DCC-GARCH test was conducted to determine any conditional correlations that impact volatility transmission over a shorter and longer duration of time between variables. The results showed that there was no unidirectional or bidirectional relationship between bitcoin and FTSE venture capital index, as well as between bitcoin and the Barclays Hedge Fund Index. The DCC model showed no volatility transmission between bitcoin and the Barclays Hedge Fund Index, whereas volatility persists between bitcoin and the FTSE Venture Capital Index, connecting risk between the financial time series with only low correlations. These findings suggest that bitcoin could be used by investors, policy makers, and hedgers for diversification in alternative investment fund portfolios.
AI text-to-video systems, such as OpenAI’s Sora, promise substantial efficiency gains in media production but also pose risks of biased outputs, opaque optimization, and deceptive content. Using the Orientation–Stimulus–Orientation–Response (O-S-O-R) model, we conduct an empirical study with 209 Chinese new media professionals and employ structural equation modeling to examine how information elaboration relates to AI knowledge, perceptions, and adoption intentions. Our findings reveal a knowledge paradox: higher objective AI knowledge negatively moderates elaboration, suggesting that centralized information ecosystems can misguide even well-informed practitioners. Building on these behavioral insights, we propose a blockchain-based governance framework that operationalizes five mechanisms to enhance oversight and trust while maintaining efficiency: Expert Assessment DAOs, Community Validation DAOs, real-time algorithm monitoring, professional integrity protection, and cross-border coordination. While our study focuses on China’s substantial new media market, the observed patterns and design principles generalize to global contexts. This work contributes empirical grounding for Web3-enabled AI governance, specifies implementable smart-contract patterns for multi-stakeholder validation and incentives, and outlines a research agenda spanning longitudinal, cross-cultural, and implementation studies.
The increasing prevalence of Maximal Extractable Value (MEV) in blockchain networks has highlighted critical challenges in achieving fair and predictable transaction ordering. On Ethereum, where block builders possess unrestricted control over transaction sequencing, users face significant risks from frontrunning and sandwich attacks, particularly within decentralized finance (DeFi) applications interacting with shared contract states. To address this issue, this paper proposes a hybrid MEV mitigation method employing Lamport-style logical clocks, designed to establish a local causal ordering mechanism within individual smart contracts. The proposed approach equips each smart contract, such as a decentralized exchange liquidity pool, with a local logical timestamp counter. Transactions submitted to the contract carry logical timestamps, enabling the enforcement of a causally consistent execution order. A key benefit of this method is that it does not necessitate alterations to Ethereum’s global consensus mechanism, thus ensuring compatibility with the current Ethereum ecosystem, as well as rollups and modular app-chain architectures. The study details the protocol design, explores various implementation strategies for both on-chain and off-chain execution environments, and addresses resilience against adversarial attempts such as timestamp manipulation and denial-of-service attacks. The primary advantage of this approach lies in its effectiveness in mitigating intra-contract MEV extraction by strictly controlling transaction reordering for conflicting state interactions, while preserving concurrency for non-conflicting transactions. Findings indicate that the use of local Lamport clocks provides a practical, low-overhead solution for MEV-sensitive applications, including decentralized exchanges and rollup sequencing systems.
This paper analyzes the time-varying herding behavior in the non-fungible token (NFTs) and cryptocurrency markets and investigates their interrelationship. Using the daily market data from January 1st, 2020 to April 30th, 2023, our study covers the period characterized by Covid and post-Covid-19 induced global financial market volatility, capturing the dynamics in the global macroeconomic system and the Federal Reserve’s interest rate policy. Based on the rolling window method, our findings show the presence of herding behavior in both markets, where herding behavior in these markets may be influenced by the major events announcements particularly those related to the Federal Reserve's interest rate policy. Vector error correction model (VECM) indicates that the NFT market impacts the price of Ethereum, thereby influencing the broader cryptocurrency market. Such finding contributes to a deeper understanding of the market dynamics. By examining herding behavior, our findings indicate that the NFT market demonstrates relative independence from the volatile prices of the cryptocurrency market, suggesting the potential diversification benefits of incorporating NFTs for investors’ portfolio construction and risk management.
سازمانهای خودگردان غیرمتمرکز (DAO)، به دلیل ویژگیهایی چون فقدان مدیریت انسانی متمرکز و ساختار فراملی، با مفهوم سنتی شخصیت حقوقی در تضاد ماهوی قرار دارند. پژوهش حاضر از توصیف این بنبست شناختهشده عبور کرده و به یک پرسش راهبردی پاسخ میدهد: نظام حقوقی ایران چگونه میتواند با الهام از مبانی فقهی و تحلیل تطبیقی تجارب نوین جهانی (مانند وایومینگ)، مدلی جدید تحت عنوان «شخصیت حقوقی الگوریتمی» را طراحی و شناسایی کند؟ این تحقیق با روش توصیفی-تحلیلی، پس از اثبات ناکارآمدی چارچوبهای فعلی برای حل بحران مسئولیت و صلاحیت قضایی، به عنوان یافته اصلی، ارکان و الزامات یک مدل مفهومی نوین را ارائه میدهد. این مدل، اهلیت و موجودیت نهاد را نه بر ارکان انسانی، بلکه بر شفافیت کد، قابلیت حسابرسی الگوریتم و معرفی یک عامل ثبتشده استوار میسازد. مقاله همچنین با بررسی تطبیقی رویکردهای جهانی و پاسخگویی به انتقادات کلیدی، اعتبار و کارآمدی مدل پیشنهادی را تقویت مینماید. نتیجهگیری پژوهش آن است که مواجهه کارآمد با DAO مستلزم عبور از راهکارهای اصلاحی و حرکت به سمت قانونگذاری جدید و ویژهای است که این شخصیت حقوقی فناورانه را به رسمیت بشناسد و ضمن فراهم آوردن بستر نوآوری اقتصادی، پاسخگویی حقوقی و قضایی این نهادها را در برابر حاکمیت تضمین نماید.
Sorina Geanina Stănescu, Constantin Aurelian Ionescu, Maria Cristina Ștefan, Luiza Ionescu · 6 authors
The agri-food sector is currently undergoing a significant digital transformation, driven by climate change, frequent supply chain disruptions, and increasing demand for transparency and food safety. This article, based on a systematic review of 113 recent studies (in line with the PRISMA guidelines), delves into how emerging digital technologies, particularly blockchain, are reshaping agri-food supply chains towards sustainability, a circular economy, and complete product traceability from production to the final consumer. The paper identifies the main enabling factors, barriers, and implementation models of blockchain and other technologies associated with Industry 4.0 (IoT, artificial intelligence, smart contracts), highlighting their role in increasing the resilience of supply chains, optimising quality control, and sustainable resource management. A key contribution of the study is the introduction of the CTSAF (Converging Technologies for Sustainable Agri-Food Chains) conceptual framework, which provides practical implications for policymakers and organisations, enabling them to make informed decisions. The results also provide valuable insights for future research, supporting the transition to a more transparent, resilient, and sustainable global food system.
I‐Fang Su, Shun-Ming Wang, Yu-Chi Chung, Yi-Hsien Tsai
Abstract In this research, we introduce an advanced approach for the detection of smart contract vulnerabilities leveraging Large Language Models (LLMs). Smart contracts are pivotal in the ecosystem of decentralized finance (DeFi), functioning as automated protocols for data management and transaction execution. The foundation of numerous blockchain-based applications lies in smart contract technology. Nevertheless, these contracts’ code vulnerabilities can become targets for malicious exploitation, leading to substantial financial damages, exemplified by the 2016 Dao smart contract incident which incurred a loss of 55 million USD. In response to such challenges, detection mechanisms for smart contract vulnerabilities have been devised, drawing upon conventional static analysis, fuzzy testing, and machine learning methodologies. Owing to the swift progression of LLMs, such as GPT, a broad spectrum of entities has adopted these models for routine operational management. By recognizing LLMs’ inherent capability to comprehend programming code, we investigate their aptitude for identifying smart contract vulnerabilities. We have integrated prompt engineering techniques, including the Chain of Thought (CoT), Plan-and-Solve, and few-shot learning, to augment the LLMs’ vulnerability detection efficacy. Furthermore, a sequence of empirical studies has been orchestrated to validate the effectiveness of our proposed prompt engineering strategies against diverse smart contract vulnerabilities.
Abstract In digital higher education, digital transformation is mandatory. Blockchain technology, with its unique features of distributed ledgers, consensus mechanisms, smart contracts, and traceability, provides a new perspective for digital educational resource platforms. In this study, a blockchain-based design was proposed for an open service platform for digital education resources in universities. The platform addresses challenges such as limited openness, complex resource copyright certification, and difficulty in effectively ensuring resource security and quality. The platform offers resource publishing, resource trading, operation management, and interface management ensuring data security using the distributed ledger of blockchain. Consensus mechanisms and smart contracts are used to ensure fairness and efficiency in platform operation and automate resource transactions. Traceability is utilized to ensure the certification and protection of resource copyrights.
The integrity, coupled with the transparency of electoral systems, is vital for the existence of a democratic society if that society is to function well. Often, conventional electronic voting mechanisms are criticized for their security vulnerabilities, with a lack of transparency, together with limited public trust. Blockchain technology has come about to be a possible enabler for trustless and immutable systems. However, such a standard, privacy-preserving, verifiable voting model remains elusive. This work seeks to fill this void with the use of a blockchain e-voting system that uses QR codes to validate voters, cryptographically ensures integrity with the EFFT-SWIFFT hash, and also handles ballots through smart contracts. A feature matrix together with a visual chart was used in a systematic literature review of 28 peer-reviewed papers to analyze and compare authentication methods, transparency techniques, consensus mechanisms, and scalability solutions. Though the analysis reveals that entities greatly underutilize advanced cryptographic primitives such as zero-knowledge proofs and post-quantum hashing, these primitives potentially improve privacy and also verifiability. Present in the proposed model is a multi-layered architecture. Also, the model can offer a secure as well as transparent solution for addressing these gaps. Blockchain-based e-voting can increase trust, reduce fraud, and broaden democratic participation, but it requires real-world validation through pilot projects and usability testing.
Open access
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques
The rapid digitization of financial services has resulted in a staggering increase in sophisticated fraud, endangering global economies and damaging public trust. The dynamic nature of current fraud is outpacing classic fraud detection systems, which frequently rely on static, rule-based methods. This study reveals a new hybrid framework that pairs distributed ledger technology for immutable transaction avoidance with Machine Learning (ML) for real-time fraud detection. The fundamental driving force is to address the inherent shortcomings of centralized systems, as well as the lack of an unchangeable audit trail in ML-only solutions. Using a range of classification algorithms, our methodology entails creating separate machine learning pathways for three important financial domains: credit card, UPI, and loan applications. A fraud verdict is subsequently produced using the top-performing model for each domain, which is determined by a thorough analysis of metrics. Through a smart contract, this decision is safely and irrevocably documented on a private blockchain. This study shows how a strong security architecture may be produced by fusing the decentralized trust and immutability of blockchain technology with the predictive performance of machine learning. The findings demonstrate that this integrated approach strengthens the integrity and dependability of digital financial transactions by achieving high performance in fraud detection as well as creating a transparent and impenetrable record.
Samuel Ejiro Uwhejevwe-Togbolo, Ajueyitse Martins Otuedon, Jacob Martins Sigah, Theresa Nkechi Ofor · 6 authors
The study examined optimized design of digital ledger posting based on virtual reality technology. The convention of Virtual Reality (VR) and Distributed Ledger Technology (DLT) is a revolutionary change in the design and interaction of digital systems. The study finds that there are several design principles and technological considerations that were critical to the implementation of VR-enhanced digital ledger systems to succeed by a thorough examination of the existing literature and case studies. This research design is a qualitative study and will involve an exploratory approach to research the topic of Virtual Reality (VR) implementation with digital ledger posting systems. The study mainly includes a literature review and case study analysis of the existing literature and case studies in order to draw best practice and practical information. Case studies are also used as one of the main methodological instruments to provide the real-life examples of VR in financial, accounting, and the sphere of supply chains. The research is aimed at gaining insight into the way VR would maximize digital ledger posting, and not the quantification of predetermined variables. It was revealed in the study that VR provides users with many chances to perceive multidimensional datasets in a way that is not possible in a traditional 2D interface. The study concluded that the ongoing development of the digital economy, these systems will be able to increase the levels of transparency, minimize errors, and promote more efficient and cooperative and resilient organizational processes.
Energy consumption in Federated Learning (FL) has emerged as a major challenge due to the growing deployment of intelligent edge devices and the increasing complexity of machine learning models. FL enables collaborative model training across decentralized data sources without transferring raw data, thereby reducing communication overhead and enhancing data privacy by design. These features make FL particularly suitable for applications in healthcare, finance, and industrial IoT, where data sensitivity and resource constraints are critical. This paper provides a comprehensive survey of energy-efficient techniques in FL, classifying them into four main categories: model compression (including pruning and quantization), communication optimization, client selection, and hardware-aware strategies. The paper presents a unified taxonomy and discusses the strengths, limitations, and trade-offs of each approach. A comparative evaluation framework is introduced to assess energy savings, model accuracy, communication cost, and deployment feasibility. By analyzing current trends and open challenges, this review offers valuable guidance for researchers and practitioners in the development of scalable, energy-aware, and privacy-preserving federated learning systems.