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
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.
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.
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.
Today, scientific research is increasingly becoming data-centric and compute-intensive, relying on data and models across distributed sources. However, challenges still exist in the traditional cooperation mode, given the high storage and computing costs, geolocation barriers, and local confidentiality regulations. The Jupyter environment has recently emerged and evolved into a vital virtual research environment for scientific computing, which researchers can use to scale computational analyses up to larger datasets and high-performance computing resources. Nevertheless, existing approaches lack robust support of a decentralized cooperation mode to unlock the full potential of decentralized collaborative scientific research, e.g., seamlessly secure data sharing. In this work, we change the basic structure and legacy norms of current research environments via the seamless integration of Jupyter with Ethereum blockchain capabilities. As such, it creates a Decentralized Virtual Research Environment (D-VRE) from private computational notebooks to a decentralized collaborative research ecosystem. We propose a novel architecture for the D-VRE and prototype some essential D-VRE elements for enabling secure data sharing with decentralized identity, user-centric agreement-making, membership, and research asset management. To validate our method, we conduct an experimental study to test all functionalities of D-VRE smart contracts and their gas consumption. In addition, we deploy the D-VRE prototype on a test net of the Ethereum blockchain for demonstration. The feedback from the studies showcases the current prototype's usability, ease of use, and potential, and suggests further improvements.
I Gede Agus Krisna Warmayana, Yuichiro Yamashita, Nobuto Oka "Decentralized Materials Data Management using Blockchain, Non-Fungible Tokens, and Interplanetary File System in Web3" Journal of Applied Data Sciences, 2025, Vol.6, No.1, p.742-752 https://doi.org/10.47738/jads.v6i1.380 掲載
Akwan Maroso, Dwi Shinta Angreni, Rizka Ardiansyah, Kadek Agus Dwiwijaya
This study investigates the application of blockchain technology in enhancing the security and authenticity of digital certificates. Addressing key challenges such as fraud and the lack of a standardized verification process, the paper proposes a comprehensive framework aimed at fortifying the integrity of digital credentials. This framework is the utilization of blockchain as a distributed ledger, serving as a tamper-proof repository for recording certification transactions. Through this decentralized ledger, each certification issuance and verification action is securely recorded, enhancing trust and transparency in the certification process. The methodology includes the integration of a decentralized ledger for immutable record-keeping and implementation of smart contracts for automated authenticity checks, and the use of cryptographic measures to ensure data security. This approach promises significant implications for various sectors reliant on credential verification, advocating for a broader adoption of blockchain in digital certificates systems.
This article discusses the issues with traditional scientific publishing and the solutions offered by DeSci. It covers problems caused by oligopoly in scientific publishing, author-reviewer-editor triangulation, predatory journals, and funding and resource allocation. These issues result in gated publishing, peer review bias, publish or perish culture, centralized funding, poor publication quality, low accessibility, low transparency, and low reproducibility. This study discusses DeSci's solutions to the aforementioned problems through blockchain technologies, including DAO, DBDAO, NFT, Zero-Knowledge Proofs, IP-NFT, and IPFS. Sample applications and projects are provided to illustrate these solutions. DeSci's solutions represent a transition from oligopoly in scientific production to the true Open Science era.
Abstract Scientific workflows are essential for many applications, enabling the configuration and execution of complex tasks across distributed resources. In this paper, we contribute an Ethereum blockchain-based scientific workflow execution manager, which distributes workflows to run on cluster computing providers that utilize the Slurm workload manager to execute them. We extended our blockchain-based autonomous resource broker called eBlocBroker, which is a DAO-based decentralized coordinator, by providing distributed workflow execution via blockchain. Through various tests, we demonstrate how our eBlockBroker autonomous organization, which is programmed as a smart contract, can manage scientific workflow submission, scheduling, and execution on cluster computing providers. The utilization of blockchain for distributed workflow execution is a new concept. We are motivated because our system has been developed with e-Science in mind where scientific workflows are widely utilized.
Soil is an indispensable resource with critical implications in various fields such as agriculture, environmental science, climate change, hydrology, ecology, and geoscience. Accuracy and accessibility of soil data are crucial for informed decision making. However, the sharing and harmonization of soil data present significant challenges, particularly owing to the lack of a comprehensive identification system that ensures privacy and stewardship in a federated data sharing framework. Moreover, the inherent heterogeneity of soil properties across space and time complicates the establishment of connections between soil profiles and their corresponding properties. To address these challenges, a novel and persistent soil-data identifier, called SoilPrint, akin to a fingerprint, was proposed. SoilPrint utilizes a mathematical algorithm to effectively integrate the properties of soil profile layers (SPLP) with Geohashes, providing an efficient solution. The incorporation of SoilPrint streamlines the data federation process within a secure and distributed ledger, eliminating the need for complex data mapping or alignment. This approach ensures data privacy throughout the sharing process and addresses concerns associated with data management. To demonstrate the practical applications of SoilPrint, a case study using soil data from Ontario, Canada was presented. The results underscored the unique identification capabilities of SoilPrint for soil profiles and their associated properties, establishing it a promising tool for soil data management. SoilPrint facilitates data tracking, reuse, and analysis, thereby enhancing the efficiency and effectiveness of soil-related research and decision-making processes.
Background: Rapid advancements in Distributed Ledger Technology (DLT), including blockchain, are foundational to a new era of digital innovation. This innovation has catalyzed the emergence of ‘Decentralized Science (DeSci),’ a new concept and movement that aims to address the challenges of modern science. Objective: Given the novelty of the field of DeSci, this study aims to provide a comprehensive definition of the term as well as explore and conceptualize shared values and guiding principles inherent to DeSci. Methods: In line with the objectives of this study, an exploratory literature review was conducted to identify and synthesize the scholarly and secondary literature. The search and selection process included six databases (PubMed, Google Scholar, Web of Science, IEEE Xplore, arXiv, and Social Science Research Network), and the search period was limited to the last 15 years, from 2008 to 2023. To identify relevant secondary literature, such as articles, reports, blog posts, and website content, a keyword search was conducted in three search engines (Google.com, Bing.com, and Yahoo.com). Owing to the novelty of the concept and movement of DeSci, the exploratory literature review was supplemented by an anonymous online-based expert survey using a combination of single-choice and open-ended questions. The experts were selected based on predefined inclusion criteria, in association with their activities in the field of DeSci. The responses to the single-choice questions were subject to statistical analysis, whereas the open-ended questions were analyzed using qualitative content analysis. Results: Seven studies were selected for evaluation as part of the search and selection process to identify relevant scholarly literature. Following the review of secondary literature, additional 24 publications were included in the analysis. In the expert survey, 39 valid datasets were collected and analyzed. Following the synthesis of the results of the exploratory literature review and expert survey, a comprehensive definition of the term ‘Decentralized Science’ (DeSci) was formulated to reflect recurring themes. As no publications that explicitly discussed or addressed the values or principles of DeSci in the exploratory literature review could be identified, a set of shared values and guiding principles for DeSci were defined based on the results of the expert survey. Conclusion: The results of this study underscore the emerging nature of DeSci, as evidenced by the limited availability of relevant information and scarcity of academic publications. While this study proposes a comprehensive definition of DeSci as well as a set of shared values and guiding principles, the results of this study highlight the importance of ongoing evaluation and validation. Furthermore, the results of this study indicate a clear need for future research in the field of DeSci, emphasizing its dynamic and developing nature.
Blockchain is a decentralized distributed ledger technology. The application of blockchain technology in the management and sharing of scientific research data in universities is conducive to improving the storage and sharing efficiency of scientific research data, and promoting the effective sharing and application of scientific research data across departments, institutions and regions. This paper analyzes the application status and problems of blockchain technology in the development of scientific research data sharing platform in colleges and universities, expounds the development ideas of scientific research data sharing mode based on blockchain technology, and proposes a digital educational resource sharing model with data standard model structure and process, data standard sharing and data storage as the main characteristics based an alliance chain. Experiments show that the proposed model has a high probability to ensure that the colleges and universities can reach a consensus and share scientific research data in alliance.
Rob J. Lewis, Kjell‐Erik Marstein, John‐Arvid Grytnes
Research centred on understanding scientists’ attitudes towards open data in ecology and evolution point to an increased acceptance of and willingness to engage in open data practices 1 , 2 , but also identifies common threads of concern which present barriers to data sharing . Mindsets concerning data as proprietary are common 3 , especially where data production is resource intensive 4 . Fears of competing research in concert with loss of exclusivity to hard earned data are pervasive 1 , 5 , 6 , 7 . This is for good reason given that current reward structures in academia focus overwhelmingly on journal prestige and high publication counts 8 , and not accredited publication of open datasets. And, then there exists reluctance of researchers to cede control to centralised repositories, citing concern over the lack of trust and transparency over the way complex data are used and interpreted 6 , 9 , 10 .