Tackling disinformation is crucial for the development of the Information Society. To do this, it is necessary to empower journalists in the production of trustworthy information, and to nurture an economic ecosystem centred on the secure circulation of content. In this contribution we present an interdisciplinary approach that aims (1) to find a balance between freedom of expression and other fundamental rights (e.g., privacy and data protection), (2) to develop business models driven by the production of genuine content, and (3) to exploit the potential of distributed ledger systems to provide media certification.
Wuqi Zhang, Zhuo Zhang, Qingkai Shi, Lu Liu ยท 8 authors
Smart contracts are susceptible to front-running attacks, in which malicious users leverage prior knowledge of upcoming transactions to execute attack transactions in advance and benefit their own portfolios. Existing contract analysis techniques raise a number of false positives and false negatives in that they simplistically treat data races in a contract as front-running vulnerabilities and can only analyze contracts in isolation. In this work, we formalize the definition of exploitable front-running vulnerabilities based on previous empirical studies on historical attacks, and present Nyx, a novel static analyzer to detect them. Nyx features a Datalog-based preprocessing procedure that efficiently and soundly prunes a large part of the search space, followed by a symbolic validation engine that precisely locates vulnerabilities with an SMT solver. We evaluate Nyx using a large dataset that comprises 513 real-world front-running attacks in smart contracts. Compared to six state-of-the-art techniques, Nyx surpasses them by 32.64%-90.19% in terms of recall and 2.89%-70.89% in terms of precision. Nyx has also identified four zero-days in real-world smart contracts.
Provenance facts, such as who made an image and how, can provide valuable context for users to make trust decisions about visual content. Against a backdrop of inexorable progress in generative AI for computer graphics, over two billion people will vote in public elections this year. Emerging standards and provenance enhancing tools promise to play an important role in fighting fake news and the spread of misinformation. In this article, we contrast three provenance enhancing technologies-metadata, fingerprinting, and watermarking-and discuss how we can build upon the complementary strengths of these three pillars to provide robust trust signals to support stories told by real and generative images. Beyond authenticity, we describe how provenance can also underpin new models for value creation in the age of generative AI. In doing so, we address other risks arising with generative AI such as ensuring training consent, and the proper attribution of credit to creatives who contribute their work to train generative models. We show that provenance may be combined with distributed ledger technology to develop novel solutions for recognizing and rewarding creative endeavor in the age of generative AI.
Nayantara K Kumar, Niranjan V Honnungar, M Sharwari Prakash, J J Lohith
In the ever-changing world of blockchain technology, the emergence of smart contracts has completely transformed the way agreements are executed, offering the potential for automation and trust in decentralized systems. Despite their built-in security features, smart contracts still face persistent vulnerabilities, resulting in significant financial losses. While existing studies often approach smart contract security from specific angles, such as development cycles or vulnerability detection tools, this paper adopts a comprehensive, multidimensional perspective. It delves into the intricacies of smart contract security by examining vulnerability detection mechanisms and defense strategies. The exploration begins by conducting a detailed analysis of the current security challenges and issues surrounding smart contracts. It then delves into established frameworks for classifying vulnerabilities and common security flaws. The paper examines existing methods for detecting, and repairing contract vulnerabilities, evaluating their effectiveness. Additionally, it provides a comprehensive overview of the existing body of knowledge in smart contract security-related research. Through this systematic examination, the paper aims to serve as a valuable reference and provide a comprehensive understanding of the multifaceted landscape of smart contract security.
Zhiyang Chen, Ye Liu, Sidi Mohamed Beillahi, Yi Li ยท 5 authors
Smart contract transactions associated with security attacks often exhibit distinct behavioral patterns compared with historical benign transactions before the attacking events. While many runtime monitoring and guarding mechanisms have been proposed to validate invariants and stop anomalous transactions on the fly, the empirical effectiveness of the invariants used remains largely unexplored. In this paper, we studied 23 prevalent invariants of 8 categories, which are either deployed in high-profile protocols or endorsed by leading auditing firms and security experts. Using these well-established invariants as templates, we developed a tool Trace2Inv which dynamically generates new invariants customized for a given contract based on its historical transaction data. We evaluated Trace2Inv on 42 smart contracts that fell victim to 27 distinct exploits on the Ethereum blockchain. Our findings reveal that the most effective invariant guard alone can successfully block 18 of the 27 identified exploits with minimal gas overhead. Our analysis also shows that most of the invariants remain effective even when the experienced attackers attempt to bypass them. Additionally, we studied the possibility of combining multiple invariant guards, resulting in blocking up to 23 of the 27 benchmark exploits and achieving false positive rates as low as 0.32%. Trace2Inv outperforms current state-of-the-art works on smart contract invariant mining and transaction attack detection in terms of both practicality and accuracy. Though Trace2Inv is not primarily designed for transaction attack detection, it surprisingly found two previously unreported exploit transactions, earlier than any reported exploit transactions against the same victim contracts.
In the ever-evolving realm of cybersecurity, the increasing integration of Metaverse systems with cutting-edge technologies such as Artificial Intelligence (AI), Blockchain, and Cloud Computing presents a host of new opportunities alongside significant challenges. This article employs a methodological approach that combines an extensive literature review with focused case study analyses to examine the changing cybersecurity landscape within these intersecting domains. The emphasis is particularly on the Metaverse, exploring its current state of cybersecurity, potential future developments, and the influential roles of AI, blockchain, and cloud technologies. Our thorough investigation assesses a range of cybersecurity standards and frameworks to determine their effectiveness in managing the risks associated with these emerging technologies. Special focus is directed towards the rapidly evolving digital economy of the Metaverse, investigating how AI and blockchain can enhance its cybersecurity infrastructure whilst acknowledging the complexities introduced by cloud computing. The results highlight significant gaps in existing standards and a clear necessity for regulatory advancements, particularly concerning blockchainโs capability for self-governance and the early-stage development of the Metaverse. The article underscores the need for proactive regulatory involvement, stressing the importance of cybersecurity experts and policymakers adapting and preparing for the swift advancement of these technologies. Ultimately, this study offers a comprehensive overview of the current scenario, foresees future challenges, and suggests strategic directions for integrated cybersecurity within Metaverse systems utilising AI, blockchain, and cloud computing.
The rise of non-fungible tokens (NFTs) and the emergence of the Metaverse is a significant paradigm shift in the digital landscape. NFTs are unique digital assets verified by blockchain technology that have revolutionized ownership and provenance in the digital realm. Concurrently, the Metaverse, a collective virtual shared space, is evolving into a multifaceted environment where NFTs play a pivotal role in shaping economies, identities, and interactions. This chapter covers NFTs the transforming digital ownership, art in the digital age, Metaverse, economy and commerce, social structures in the Metaverse.
The spread of the Metaverse has created new moral and legal challenges, especially when it comes to protecting against fraud. This study explores the legal complexities surrounding Ethereum-specific metaverse transactions and provides guidance on how to identify and address fraudulent activity. The focus of this investigation is the field of fraud detection in the Ethereum environment. Current methods of detecting fraud, such as Blockchain analysis and machine learning-based algorithms, are carefully examined. Next, a new approach to fraud detection is put out, which is predicated on a collection of seven machine learning algorithms: K-Nearest Neighbours (KNN), Decision Tree, Support Vector Machine (SVM), Random Forest, KNN, XGBoost (XGB), and Artificial Neural Network (ANN). The results of this study are carefully outlined, demonstrating the accuracy, recall, F1 score, and precision that each of the previously listed algorithms demonstrated. Moreover, this article lays out possible directions for further research, including the incorporation of group approaches and the investigation of creating characteristics to strengthen fraud detection abilities. This academic paper provides a significant and novel insight into the identification of fraudulent activity in Ethereum transactions, highlighting the potential benefits of using machine learning algorithms for this kind of discernment.
Reentrancy vulnerability as one of the most notorious vulnerabilities, has been a prominent topic in smart contract security research. Research shows that existing vulnerability detection presents a range of challenges, especially as smart contracts continue to increase in complexity. Existing tools perform poorly in terms of efficiency and successful detection rates for vulnerabilities in complex contracts. To effectively detect reentrancy vulnerabilities in contracts with complex logic, we propose a tool named SliSE. SliSEโs detection process consists of two stages: Warning Search and Symbolic Execution Verification . In Stage I, SliSE utilizes program slicing to analyze the Inter-contract Program Dependency Graph (I-PDG) of the contract, and collects suspicious vulnerability information as warnings. In Stage II, symbolic execution is employed to verify the reachability of these warnings, thereby enhancing vulnerability detection accuracy. SliSE obtained the best performance compared with eight state-of-the-art detection tools. It achieved an F1 score of 78.65%, surpassing the highest score recorded by an existing tool of 9.26%. Additionally, it attained a recall rate exceeding 90% for detection of contracts on Ethereum. Overall, SliSE provides a robust and efficient method for detection of Reentrancy vulnerabilities for complex contracts.
Outsourcing computation enables a weak client to expand its computational power as the need arises. A basic requirement of outsourcing computation is the guarantee that the computation result is correct. Cryptographic solutions that provide verifiability for the computation result when the computation is outsourced to a single server, are complex and fragile. We consider the intuitive approach of verifiable computation, called verifiable computation by replication, when the computation is replicated on multiple servers, and a referee decides the result of the final computation using the outputs of all servers. We consider the case when a smart contact is used as the referee. We propose a security model in the Universal Composability (UC) framework of Canetti, and design a 2-server and an n-server protocol with proved security in our model. Our protocols build on the Refereed Delegation of Computation (RDoC) framework of Canetti, Riva, and Rothblum, underline the challenges of using a smart contract as a referee, and address those challenges in the designed protocols. We give the efficiency analysis of the protocols, provide a proof of concept implementation for our protocols using Ethereum smart contact, and give concrete cost values for an example computation.
The growing reliance on artificial intelligence in decision-making processes has intensified debates over bias, fairness, and accountability in algorithmic governance. While deep learning models deliver unprecedented predictive performance, their โblack boxโ nature has undermined transparency and public trust, particularly in high-stakes applications such as finance, healthcare, and digital public services. Explainable AI (XAI) has emerged to address this gap by making model reasoning interpretable, yet explainability alone cannot guarantee fairness without verifiable systems of identity and accountability. This study proposes a framework that integrates explainable deep learning with decentralized identity (DID) systems to combat bias, enhance trust, and ensure equitable governance outcomes. In this framework, explainable deep learning models provide human-understandable insights into algorithmic decisions, enabling stakeholders to evaluate reasoning processes. Meanwhile, decentralized identity systems built on blockchain technologies ensure that individuals retain control over their digital identities, reducing risks of centralized manipulation and exclusion. By linking interpretable models with verifiable identity protocols, algorithmic governance can achieve both transparency and fairness while protecting privacy. The integration enables bias detection and correction at both the model and system levels: interpretable models flag discriminatory features, while decentralized identity guarantees equitable access across diverse populations. Applications in digital voting, welfare distribution, and credit scoring illustrate how the framework strengthens accountability and prevents systemic marginalization. Ultimately, combining explainable deep learning with decentralized identity provides a path toward trustworthy and fair algorithmic governance, where decisions are not only accurate but also transparent, inclusive, and ethically aligned with societal values.
Foundation models including large language models (LLMs) are increasingly attracting interest worldwide for their distinguished capabilities and potential to perform a wide variety of tasks. Nevertheless, people are concerned about whether foundation model based AI systems are properly governed to ensure trustworthiness of foundation model based AI systems and to prevent misuse that could harm humans, society and the environment. In this paper, we identify eight governance challenges of foundation model based AI systems regarding the three fundamental dimensions of governance: decision rights, incentives, and accountability. Furthermore, we explore the potential of blockchain as a solution to address the challenges by providing a distributed ledger to facilitate decentralised governance. We present an architecture that demonstrates how blockchain can be leveraged to realise governance in foundation model based AI systems.
This article investigates the intricate dynamics of data monopolies, referred to as โdata-opoliesโ, and their implications for democratic erosion. Data-opolies, typically embodied by large technology corporations, accumulate extensive datasets, affording them significant influence. The sustainability of such data practices is critically examined within the context of decentralized Web3 technologies amidst Artificial Intelligence (AI) disruption. Additionally, the article explores emancipatory datafication strategies to counterbalance the dominance of data-opolies. It presents an in-depth analysis of two emergent phenomena within the decentralized Web3 emerging landscape: People-Centered Smart Cities and Datafied Network States. The article investigates a paradigm shift in data governance and advocates for joint efforts to establish equitable data ecosystems, with an emphasis on prioritizing data sovereignty and achieving digital self-governance. It elucidates the remarkable roles of (i) blockchain, (ii) decentralized autonomous organizations (DAOs), and (iii) data cooperatives in empowering citizens to have control over their personal data. In conclusion, the article introduces a forward-looking examination of Web3 decentralized technologies, outlining a timely path toward a more transparent, inclusive, and emancipatory data-driven democracy. This approach challenges the prevailing dominance of data-opolies and offers a framework for regenerating datafied democracies through decentralized and emerging Web3 technologies.
Data-driven modeling using Artificial Intelligence (AI) is envisioned as a key enabling technology for Zero Touch Network (ZTN) management. Specifically, AI has shown huge potential for automating and modeling the threat detection mechanism of complicated wireless systems. The current data-driven AI systems, however, lack transparency and accountability in their decisions, and assuring the reliability and trustworthiness of the data collected from participating entities is an important obstacle to threat detection and decision-making. To this end, we integrate smart contracts with eXplainable AI (XAI) to design a robust cybersecurity framework for ZTN. The proposed framework uses a blockchain and smart contract-enabled access control and authentication mechanism to ensure trust among the participating entities. Additionally, with the collected data, we designed Digital Twins (DTs) for simulating the attack detection operation in the ZTN environment. Specifically, to provide a platform for analysis and the development of an Intrusion Detection System (IDS), the DTs are equipped with a variety of process-aware attack scenarios. A Self Attention-based Long Short Term Memory (SALSTM) network is used to evaluate the attack detection capabilities of the proposed framework. Furthermore, the explainability of the proposed AI-based IDS is achieved using the SHapley Additive exPlanations (SHAP) tool. The experimental results using N-BaIoT and a self-generated DTs dataset confirm the superiority of the proposed framework over some baseline and state-of-the-art techniques. โข A new robust cybersecurity framework for ZTN is proposed by integrating smart contracts with eXplainable AI. โข To ensure secure communication, a novel blockchain-enabled key establishment and access control mechanism is proposed that authenticates the participating entities in ZTN. The temper-proof property of blockchain ensures high integrity of the data enrichment and builds trust between the participating entities of blockchain network. A smart contract enabled Proof-of-Authority (PoA) consensus mechanism is used to verify and validate the transactions or data. โข The authenticated data from smart contract and blockchain-enabled authentication scheme is used to design DT for simulating the attack detection operation in ZTN environment. In particular, the DT is set up with a range of process aware attack scenarios to provide a platform for study and the creation of Intrusion Detection System (IDS). To assess the proposed frameworkโs ability to identify attacks, a Self Attention-based Long Short Term Memory (SALSTM) network is deployed. Additionally, utilizing the SHapley Additive exPlanations (SHAP) tool, the proposed AI-based IDS is made explainable. โข Through experiments using an actual DT simulated dataset and state-of-the-art intrusion dataset (N-BaIoT) is used to evaluate the proposed framework. The outcomes are compared with some baseline and state-of-the-art techniques to show the effectiveness of the proposed cybersecurity framework.
Chhavi Yadav, Amrita Roy Chowdhury, Dan Boneh, Kamalika Chaudhuri
Machine learning models are increasingly used in societal applications, yet legal and privacy concerns demand that they very often be kept confidential. Consequently, there is a growing distrust about the fairness properties of these models in the minds of consumers, who are often at the receiving end of model predictions. To this end, we propose \name -- a system that uses Zero-Knowledge Proofs (a cryptographic primitive) to publicly verify the fairness of a model, while maintaining confidentiality. We also propose a fairness certification algorithm for fully-connected neural networks which is befitting to ZKPs and is used in this system. We implement \name in Gnark and demonstrate empirically that our system is practically feasible. Code is available at https://github.com/infinite-pursuits/FairProof.
Bianca-Mihaela Ganescu, Jonathan PasseratโPalmbach
Generative AI, exemplified by models like transformers, has opened up new possibilities in various domains but also raised concerns about fairness, transparency and reliability, especially in fields like medicine and law. This paper emphasizes the urgency of ensuring fairness and quality in these domains through generative AI. It explores using cryptographic techniques, particularly Zero-Knowledge Proofs (ZKPs), to address concerns regarding performance fairness and accuracy while protecting model privacy. Applying ZKPs to Machine Learning models, known as ZKML (Zero-Knowledge Machine Learning), enables independent validation of AI-generated content without revealing sensitive model information, promoting transparency and trust. ZKML enhances AI fairness by providing cryptographic audit trails for model predictions and ensuring uniform performance across users. We introduce snarkGPT, a practical ZKML implementation for transformers, to empower users to verify output accuracy and quality while preserving model privacy. We present a series of empirical results studying snarkGPT's scalability and performance to assess the feasibility and challenges of adopting a ZKML-powered approach to capture quality and performance fairness problems in generative AI models.
Abstract The article aims to investigate the potential of blockchain technology in mitigating certain cybersecurity risks associated with artificial intelligence (AI) systems. Aligned with ongoing regulatory deliberations within the European Union (EU) and the escalating demand for more resilient cybersecurity measures within the realm of AI, our analysis focuses on specific requirements outlined in the proposed AI Act. We argue that by leveraging blockchain technology, AI systems can align with some of the requirements in the AI Act, specifically relating to data governance, record-keeping, transparency and access control. The study shows how blockchain can successfully address certain attack vectors related to AI systems, such as data poisoning in trained AI models and data sets. Likewise, the article explores how specific parameters can be incorporated to restrict access to critical AI systems, with private keys enforcing these conditions through tamper-proof infrastructure. Additionally, the article analyses how blockchain can facilitate independent audits and verification of AI system behaviour. Overall, this article sheds light on the potential of blockchain technology in fortifying high-risk AI systems against cyber risks, contributing to the advancement of secure and trustworthy AI deployments. By providing an interdisciplinary perspective of cybersecurity in the AI domain, we aim to bridge the gap that exists between legal and technical research, supporting policy makers in their regulatory decisions concerning AI cyber risk management.
Amar Johri, Anu Sayal, N Chaithra, Janhvi Jha ยท 8 authors
The "Metaverse," a term popularized by Neal Stephenson's novel Snow Crash, has been discussed in the science fiction community for decades, but technological advancements have only recently made it a reality. The Metaverse is an all-encompassing, interconnected virtual environment where users can freely communicate with one another and digital content. This article examines how various technologies, primarily Virtual Reality (VR) and Augmented Reality (AR), have contributed to the development of the Metaverse (AR). These innovations have revolutionized the way we interact with digital media by enabling us to have genuine, realistic experiences. In addition, we examine the Metaverse technologies that make it possible to construct a fully realized, functional virtual world. Among these are recent advances in artificial intelligence (AI), cryptocurrencies, spatial and peripheral computing, and other fields. Our research investigates the advantages and disadvantages of these technologies, as well as how they may influence the future of the Metaverse. Furthermore, the article explores the darker aspects of the Metaverse, particularly the emergence of the "dark verse," which underscores the potential for organized illicit activities within the Internet due to insufficient oversight and governance of the Metaverse.
Artificial Intelligence (AI) models are increasingly integrated into high-stakes domains such as finance, healthcare, autonomous systems, and legal decision-making. As their influence expands, concerns about accountability, fairness, transparency, and regulatory compliance have become central to both researchers and practitioners. One of the key challenges is auditing AI models in a manner that is tamper-proof, verifiable, and compliant with evolving regulatory frameworks. Traditional auditing mechanisms rely heavily on centralized logs and organizational trust, which creates vulnerabilities in terms of manipulation, incomplete records, and opacity in data flows. Blockchain technologyโowing to its immutable, decentralized, and transparent natureโoffers a powerful paradigm for establishing data provenance in AI auditing. By ensuring traceability of datasets, model updates, training logs, and inference outcomes, blockchain can provide regulators, stakeholders, and organizations with reliable audit trails. This paper presents a comprehensive exploration of blockchain-powered data provenance for AI model audits. It analyzes the limitations of current audit systems, evaluates how distributed ledger systems can strengthen accountability, and proposes an integrated framework that combines blockchain with cryptographic verification, zero-knowledge proofs, and federated logging to ensure verifiability without exposing sensitive data. The study synthesizes contributions from literature, presents a methodology for deploying blockchain-based provenance systems in AI pipelines, and evaluates potential results in terms of efficiency, compliance traceability, and security. Simulation experiments suggest that blockchain-enabled audits improve transparency, reduce fraudulent activities in AI operations, and enhance compliance readiness by more than 50% compared to traditional audit approaches.