M. Mut-Puigserver, M. Magdalena Payeras-Capellà, Rosa Pericàs-Gornals, Jaume Ramis
This paper introduces FAIR (Federated Agreement for Independent Resolution), a cross-chain protocol designed to enhance scalability, transparency, and auditability in decentralized decision-making processes. Unlike traditional blockchainbased voting systems that rely on a single global chain, FAIR leverages a federated model in which local decisions are conducted on independent blockchains and subsequently aggregated into a global result through a secure cross-chain mechanism. The protocol employs Soulbound Tokens (SBTs) and Rejectable SBTs (RejSBTs) to ensure verifiability, immutability, and explicit acceptance of results, thereby strengthening both the integrity and security of cross-chain operations. We formalize the protocol, provide smart contract implementations, and illustrate its applicability in diverse contexts such as multinational corporations, district-based elections, and decentralized autonomous organizations. FAIR demonstrates that a federated and tokenized approach to decision-making enables efficient large-scale participation while maintaining strong guarantees of trust, transparency, and robustness.
The ethical tension surrounding AI-generated art often arises from misconceptions that anthropomorphize the algorithmic process. The accusation that “AI steals human creativity” overlooks the mediating role of human design and data literacy. This paper reframes the debate as a problem of informational asymmetry rather than morality. It proposes that Non-Fungible Tokens (NFTs) and Digital Object Identifiers (DOIs) can visualize and authenticate the flow of creative tension within a transparent ecosystem. NFTs serve as formal anchors—recording authorship, signature, and temporal origin—while DOIs preserve the conceptual framework and creative process. When linked, these two systems transform authorship into a traceable circulation of knowledge, allowing the boundary between plagiarism, homage, and originality to be objectively determined. This dual-layer provenance model presents an ethical infrastructure for creation in the age of generative AI.
Open access
2 source records
Scientific Computing and Data Management
Ethics and Social Impacts of AI
Artificial Intelligence in Healthcare and Education
This paper addresses one of the most noteworthy issues in the recent virtual asset market, the privacy concerns related to token transactions of Real-World Assets tokens, known as RWA tokens. Following the advent of Bitcoin, the virtual asset market has experienced explosive growth, spawning movements to link real-world assets with virtual assets. However, due to the transparency principle of blockchain technology, the anonymity of traders cannot be guaranteed. In the existing blockchain environment, there have been instances of protecting the privacy of fungible tokens (FTs) using mixer services. Moreover, numerous studies have been conducted to secure the privacy of non-fungible tokens (NFTs). However, due to the unique characteristics of RWA tokens and the limitations of each study, it has been challenging to achieve the goal of anonymity protection effectively. This paper proposes a new token trading platform, the ARTeX, designed to resolve these issues. This platform not only addresses the shortcomings of existing methods but also ensures the anonymity of traders while enhancing safeguards against illegal activities.
Reilly Smethurst, Orestis Papageorgiou, Ben Egliston
Blockchain-tokenised media is kitsch. We examined OpenSea’s top 50 collections of non-fungible tokens (NFTs) as well as the first two NFTs acquired by New York’s Museum of Modern Art (MoMA). We concomitantly developed two concepts: oppositional kitsch, and historically informed kitsch. OpenSea’s profile picture collections exemplify oppositional kitsch: they are described by their creators as anti-normal, degenerate or animalistic. CryptoPunks and Bored Apes are the best-known examples. Historically informed kitsch is, by contrast, pleasant and reassuring. MoMA’s first two NFTs exemplify historically informed kitsch: they are produced by artificial intelligence (AI) models that rely on historical data. For Unsupervised – Machine Hallucinations – MoMA , Refik Anadol trained an AI model with images from MoMA’s GitHub archive. The Unsupervised NFTs are mementos of individuals’ encounters with the work at MoMA. For 3FACE , Ian Cheng created an AI model that draws on blockchain transaction histories. The AI model generates tokenised portraits of individuals based on their transactions. Oppositional kitsch and historically informed kitsch both valorise the individual consumer. OpenSea’s oppositional kitsch lets consumers identify with cartoon figures like CryptoPunks and Bored Apes that are marketed as avatars (alter-egos) and profile pictures. MoMA’s historically informed kitsch sells mementos of individuals’ experiences and portraits that are based on individuals’ consumption choices. Our conception of blockchain-tokenised media as kitsch is primarily informed by the philosophers Alain Badiou and Thorsten Botz-Bornstein as well as the media theorist Jean Baudrillard. These three thinkers treat kitsch as a category of sophistic, liberal-cum-libertarian culture.
Efficient and secure sharing of scientific data remains a key challenge in the Open Science framework, especially in terms of data authenticity, provenance and privacy. Traditional digital repositories improve access but often lack decentralized mechanisms that guarantee integrity and traceability. Blockchain technology provides a potential solution through tamper-proof records and distributed consensus, while Zero Knowledge Proofs (ZKP) can enhance privacy protection. This study explores how blockchain and ZKP can be integrated for decentralized scientific data management. A systematic literature review reveals limited application of these combined technologies in Open Science, highlighting a research gap and the need for solutions that support transparent, secure and privacy-preserving data sharing in accordance with FAIR principles.
Abstract – This Blockchain technology is driving a major transformation in the healthcare industry by providing a secure and decentralized framework for managing sensitive medical records. Unlike conventional systems that rely on centralized databases—often vulnerable to cyber-attacks and unauthorized alterations—blockchain operates on a distributed ledger where recorded data is immutable. This immutability significantly enhances data integrity and reduces the risk of security breaches. A key strength of blockchain in healthcare is its ability to ensure privacy. Through encryption and decentralized control, access to medical information is restricted to authorized users only. Additionally, blockchain enables efficient and secure data sharing among hospitals, clinics, and specialists, overcoming challenges posed by fragmented or inconsistent health records. This improves the speed and accuracy of patient care. Smart contracts further enhance the system by automating access permissions and updates based on predefined rules. These self-executing protocols minimize manual intervention, reduce administrative overhead, and lower the chance of human error. Most importantly, blockchain empowers patients by giving them full control over their personal health data. Patients can choose who accesses their records, fostering transparency and trust between healthcare providers and individuals. This patient-centric approach encourages active participation in healthcare decisions and supports a more collaborative care environment. Key Words: Blockchain Technology, Medical Records Management, Decentralized Systems, Data Privacy, Smart Contracts, Patient-Centric Healthcare, Secure Data Sharing, Interoperability, Tamper-Proof Records, Healthcare Automation, Distributed Ledger, Access Control, Digital Health Transformation
The recent EU regulation on Markets in Crypto Assets Regulation (MiCA) represents significant progress in establishing a multi-jurisdiction framework for crypto-assets that will enable the greater participation of consumers in the digital assets industry. One type of token recognized by MiCA is the asset-referenced token, where the value-bearing physical asset being referenced by the token is external to the token. We discuss several design considerations for the on-chain and off- chain metadata for the asset-referenced token that represents physical real-world assets. The EU Data Spaces provides an interesting data management framework for the off-chain metadata underpinning the MiCA asset-referenced tokens, including asset definition schemas, asset profiles, digitized asset records, and tokenized asset records. The Web3 decentralized registries for assets-related metadata should be a promising application of the data spaces framework in the EU.
Jens Ernstberger, Jan Lauinger, Yulin Wu, Arthur Gervais · 5 authors
Transport Layer Security (TLS) is foundational for safeguarding client-server communication. However, it does not extend integrity guarantees to third-party verification of data authenticity. If a client wants to present data obtained from a server, it cannot convince any other party that the data has not been tampered with. TLS oracles ensure data authenticity beyond the client-server TLS connection, such that clients can obtain data from a server and ensure provenance to any third party, without server-side modifications. Generally, a TLS oracle involves a third party, the verifier, in a TLS session to verify that the data obtained by the client is accurate. Existing protocols for TLS oracles are communication-heavy, as they rely on interactive protocols. We present ORIGO, a TLS oracle with constant communication. Similar to prior work, ORIGO introduces a third party in a TLS session, and provides a protocol to ensure the authenticity of data transmitted in a TLS session, without forfeiting its confidentiality. Compared to prior work, we rely on intricate details specific to TLS 1.3, which allow us to prove correct key derivation, authentication and encryption within a Zero Knowledge Proof (ZKP). This, combined with optimizations for TLS 1.3, leads to an efficient protocol with constant communication in the online phase. Our work reduces online communication by 375× and online runtime by up to 4.6×, compared to prior work.
Francisco J. Díaz, Carolina Menchaca, Lukas Weidener
Introduction The scientific community is increasingly interested in leveraging decentralized technologies to address systemic challenges such as the reputation economy, the monopolization of academic publishing, and the replication crisis. This study presents an analysis of the Decentralized Science (DeSci) landscape in 2023, focusing on organizational structures, technological foundations, and funding mechanisms of DeSci organizations. Methods A 16-question survey was distributed to DeSci organizations between December 2023 and April 2024, and responses from 49 projects were analyzed using quantitative and qualitative methods. Results Results highlight the prominent role of Ethereum as the dominant blockchain platform in DeSci, the varied applications of blockchain in scientific processes, and a significant emphasis on community building and infrastructure development. Funding sources within the ecosystem are moving towards partnerships with more traditional organizations, including academia. However, most projects lack DAO features for governance. It remains uncertain whether they will adopt more DAO-like structures in the future or deploy a different organizational model. Discussion Our findings offer a comprehensive overview of the progress and challenges facing the DeSci ecosystem, including slow project progression due to leadership issues and limited funding for most DeSci projects. By identifying key patterns and areas for improvement, this study contributes to a deeper understanding of the factors driving success and sustainability in DeSci.
Rashid Ul Haq, Rahim Khan, Fahad Alturise, Shafrida Sahrani · 6 authors
Recent technological advances have enabled researchers to investigate various novel approaches utilized to manage allograft transplants and overcome the challenges of conventional centralized systems. The rising need for transparency, efficiency, and, especially, security in this highly sensitive medical procedure necessitates the use of decentralized solutions like blockchain rather than existing centralized approaches. However, the current state of research is theoretical and unproven, and allograft management lacks any reliable, cost-effective, or data-proven solution. In this paper, we propose an Ethereum blockchain-based allograft transplantation management system that can address all of those issues linked to the existing solutions. The proposed approach aims to enhance traceability, transparency, and data provenance across the entire allograft transplant process. We present six reliable and cost-efficient algorithms, as well as a comprehensive system architecture, to provide valuable insight into system implementation complexity. We have designed an efficient smart contract implementing the proposed algorithms to ensure flawless execution of allograft donation, transportation, and transplantation. We conduct thorough tests, validation, security, cost, throughput, and latency assessments of the system in order to contrast its effectiveness with existing solutions and results shows that our solution is cost-effective, as well as secure and efficient. We generalized the proposed solution so that, with minimal changes, it could be used for other problems and addressed some of the technical and ethical challenges.
Modern enterprises generate vast volumes of data across distributed applications, cloud platforms, and digital services. Traditional centralized data governance models struggle to scale in such complex environments, leading to data silos, inconsistent governance enforcement, and limited data accessibility. Autonomous data platforms supported by artificial intelligence (AI) offer a promising solution by integrating self-service infrastructure, automated governance mechanisms, and intelligent metadata management. AI-driven governance frameworks can automate tasks such as data discovery, classification, lineage tracking, anomaly detection, and compliance monitoring. This article explores the architectural foundations of autonomous data platforms and examines how AI-driven governance enables scalable, decentralized, and trustworthy data ecosystems. Drawing on emerging concepts such as data mesh architectures, federated governance models, and responsible AI frameworks, the paper proposes a conceptual model for building intelligent and self-governing enterprise data platforms. In such environments, machine learning algorithms continuously analyze data flows, schema evolution, usage patterns, and policy compliance to dynamically enforce governance rules and improve data quality. Metadata-driven architectures further enable automated cataloging, semantic enrichment, and real-time lineage tracking, allowing organizations to maintain transparency and accountability across complex data pipelines. By embedding governance directly into the data infrastructure, autonomous platforms reduce operational overhead while empowering domain teams to manage their own data products within standardized governance policies. Furthermore, the integration of explainable AI techniques and policy-aware automation ensures that governance decisions remain auditable, fair, and aligned with regulatory requirements. Ultimately, the convergence of AI, distributed data architectures, and intelligent metadata management provides a scalable foundation for building resilient, adaptive, and trustworthy enterprise data ecosystems capable of supporting advanced analytics, machine learning, and data-driven decision-making.
As blockchain and distributed ledger technologies continue to evolve, the management of digital identity ownership and its security have increasingly come under scrutiny. The advent of decentralized identity (DID) technologies aims to return control of identity to users. However, existing DID resolution schemes often rely on centralized management, which can lead to single points of failure, security vulnerabilities, insufficient scalability, and privacy concerns. To address these challenges, this paper introduces a novel solution named CMS. CMS leverages blockchain smart contracts and InterPlanetary File System (IPFS) decentralized storage to implement a consortium management approach. It drives the management of a universal resolver through a voting mechanism of smart contracts and utilizes IPFS for distributed storage, thereby enhancing system transparency, stability, and the persistent security of data. Although this approach increases the technical complexity of the system and may impact performance, it significantly enhances management transparency and data security.
Domingo Ranieri, Alessandro COSTANTINI, Barbara Martelli
In recent years, blockchain has emerged as a promising new technology to manage trusted information, making it easier for companies to access and use critical data while maintaining the security of this information. Permissioned blockchains, unlike permissionless ones, restrict access to a select group of certified entities. They ensure a controlled and secure environment where only authorized participants can join the network and perform operations, a peculiar aspect in sectors where data sensitivity, confidentiality, and limited access are crucial. Tracking operations performed on the data and guaranteeing reproducibility of research through workflow reconstruction upon data processing become very important in different sectors ranging from scientific communities to private companies and health. This is the case of the present activity, where the implementation of a permissioned blockchain system aimed at ensuring data immutability, operations traceability, and the ability to reproduce workflows is presented and discussed. In such regards, we work with Hyperledger Fabric, an enterprise-grade permissioned distributed ledger platform that offers modularity and versatility for a broad set of industry use cases.
Siji Ma, Juanjuan Li, Sangtian Guan, Qinghua Ni · 8 authors
The development of the scientific publishing system has remarkably enhanced global accessibility to research findings and substantially increased the visibility and dissemination of academic publications. However, significant challenges still exist in effectively safeguarding the intellectual property rights of contributors, such as the unauthorized usage of materials, the complexity of enforcing intellectual property rights across various legal jurisdictions, and high instances of plagiarism and content misuse. Additionally, financial barriers related to open access may restrict the participation of economically disadvantaged researchers, potentially biasing scientific records towards more affluent research initiatives. To address these issues, a novel decentralized framework is formulated to ensure truly open access. This framework leverages blockchain for immutable record-keeping and clear attribution of authorship, to prevent unauthorized usage and plagiarism. Besides, it also utilizes a copyright-sharing model based on decentralized autonomous organizations and operations (DAOs), where smart contracts automatically enforce copyright and access policies to ensure fair compensation for authors and researchers. Furthermore, the copyright sharing model based on non-fungible tokens (NFT) and gradual ownership optimization (GOO) mechanism is proposed to ensure fair and accurate recognition and compensation for scholarly contributions.
This report introduces the Grant Maturity Index (GMI), a novel evaluative framework designed to assess the maturity and operational effectiveness of Web3 grant programs. As Web3 continues to develop, the decentralized nature of these programs brings both opportunities and challenges, particularly when it comes to governance, transparency, and community engagement. Traditional funding models are often governed by standardized processes, but Web3 grants lack such consistency, making it difficult for grant operators to measure the long-term success of their programs.The Grant Maturity Index (GMI) was created through exploratory applied research to address this gap. Inspired by the World Bank's GovTech Maturity Index (GTMI), the GMI is tailored specifically for the decentralized Web3 ecosystem. The GMI evaluates key dimensions of grant programs governance, transparency, operational efficiency, and community engagement, providing grant operators with a clear benchmark for assessing and improving their programs. The primary objectives of this research are to, first, identify the structural indicators that adequately describe Web3 grant programs. Second, to describe optimal outcomes for programs by evaluating their maturity across key operational areas. The GMI is applied to four major Ethereum Layer 2 grant programs, namely Arbitrum, Mantle, Taiko Labs, and Optimism. These case studies highlight areas where Web3 grant programs require improvement, particularly in standardizing processes, enhancing transparency, and increasing community participation.
The rapid dynamics of cryptocurrency markets and the specific convolution of blockchain technology involve both challenges and opportunities of implementing Large Language Models in this area. In the present research, we consider the process of fine-tuning and applying LLMs in the cryptocurrency sector to meet its specific needs. Through the comprehensive analysis of the dataset rationale and model’s preparation, as well as multiple practical implications in cryptocurrency workflows, it is possible to demonstrate that LLMs significantly contribute to cryptocurrency analytics, fraud identification, smart contract processing, and customer interaction potential. The paper also addresses the issues of the cryptocurrency sector, such as security, privacy, and regulation, and proposes recommendations for further research and practical implementation.
Dincy R. Arikkat, Mert Cihangiroglu, Mauro Conti, Rafidha Rehiman K. A. · 7 authors
The rise of IT-dependent operations in modern organizations has heightened their vulnerability to cyberattacks. Organizations are inadvertently enlarging their vulnerability to cyber threats by integrating more interconnected devices into their operations, which makes these threats both more sophisticated and more common. Consequently, organizations have been compelled to seek innovative approaches to mitigate the menaces inherent in their infrastructure. In response, considerable research efforts have been directed towards creating effective solutions for sharing Cyber Threat Intelligence (CTI). Current information-sharing methods lack privacy safeguards, leaving organizations vulnerable to proprietary and confidential data leaks. To tackle this problem, we designed a novel framework called SeCTIS (Secure Cyber Threat Intelligence Sharing), integrating Swarm Learning and Blockchain technologies to enable businesses to collaborate, preserving the privacy of their CTI data. Moreover, our approach provides a way to assess the data and model quality and the trustworthiness of all the participants leveraging some validators through Zero Knowledge Proofs. Extensive experimentation has confirmed the accuracy and performance of our framework. Furthermore, our detailed attack model analyzes its resistance to attacks that could impact data and model quality. • Definition of a Swarm Learning approach for collaborative CTI. • Definition of a Blockchain-based solution for privacy preservation in CTI sharing. • Secure CTI validation using a consensus mechanism and Zero-Knowledge Proof.
Die Masterarbeit untersucht die Speicherung und Performanzanalyse von Provenance-Daten mithilfe einer Blockchain. Der Fokus liegt auf der Nutzung einer privaten Blockchain zur Speicherung von Provenance-Graphen und deren Effizienzbewertung unter verschiedenen Bedingungen.
This chapter outlines initiatives taken by publishers over the years as technology has developed; these included early and unsuccessful experiments with multimedia products in the trade sector with later more successful ventures in the educational and academic sectors. It charts the move of database publishing from CD-ROMs to online services and the move from DVDs for video content to online streaming services. Coverage is given to the powerful video games industry, non-fungible tokens (NFTs) and the development of handheld electronic devices including dedicated e-readers, tablets and increasingly sophisticated smartphones, all of which may provide platforms for copyright content. Coverage is also given to enhanced e-books and apps, many of which have been produced by publishers themselves, often working with software developers. The chapter flags the increased popularity of podcasts, although these rarely feature complete copyright works. Coverage is given to several legal disputes between the major trade publishers and Apple and Amazon on e-book pricing policies. In the academic and professional sectors, coverage is provided on aggregators which supply curated collections of e-books from a range of publishers to institutions on a subscription basis, with rightsholders normally receiving a share of revenue defined as sales rather than as licence income; this sector has also seen a rise in textbook rental models. Publishers need to acquire a suitably wide range of electronic rights in their head contracts with authors; this chapter provides a checklist of issues which publishers should raise with would-be digital licensees, in particular with regard to payment models, and a list of key contractual points for licence arrangements, whether for verbatim use of copyright content or for inclusion in multimedia products.
Library Collection Development and Digital Resources