Mikel Cortes-Goicoechea, Csaba Király, Dmitriy Ryajov, José L. Muñoz · 5 authors
Scalability in blockchain remains a significant challenge, especially when prioritizing decentralization and security. The Ethereum community has proposed comprehensive data-sharding techniques to overcome storage, computational, and network processing limitations. In this context, the propagation and availability of large blocks become the subject of research to achieve scalable data-sharding. This paper provides insights after exploring the usage of a Kademlia-based Distributed Hash Table (DHT) to enable Data Availability Sampling (DAS) in Ethereum. It presents a DAS-DHT simulator to study this problem and validates the results of the simulator with experiments in a real DHT network, InterPlanetary File System (IPFS). Our results help us understand what parts of DAS can be achieved based on existing Kademlia DHT solutions and which ones cannot. We discuss the limitations of DHT solutions and discuss other alternatives.
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.
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 technology—owing to its immutable, decentralized, and transparent nature—offers 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.
Fan Yang, Mohammad Zoynul Abedin, Yanan Qiao, Lvyang Ye
Digital platforms are experiencing a growing presence of generative artificial intelligence (AI) content, raising concerns due to the prevalence of misinformation that disrupts market integrity. Consequently, the development of effective regulatory measures for overseeing generative AI content becomes imperative. This necessitates the establishment of mechanisms to detect and filter out inaccuracies, ensuring compliance with regulatory requirements. In addition, collaboration among experts, regulators, and AI developers is essential to encourage responsible AI deployment on digital platforms. Successful governance hinges on principles of transparency, accountability, and proactive risk management to navigate the evolving generative AI on digital platforms. Therefore, in order to address the security issues currently faced by artificial intelligence generated content (AIGC), this article first proposes a method of efficient cache mechanism for AIGC content. The secure method of determining the identity of AIGC content owners is proposed based on blockchain technology. Subsequently, it suggests mechanisms for access control and data encryption for generated content within a blockchain environment. Finally, it presents an efficient data supervision mechanism tailored to the AIGC environment. The methods outlined in this article aim to enhance security from three perspectives: protection of content creators' identities, safeguarding data security, and ensuring effective data supervision within the AIGC framework. The experimental results further confirm that our proposed method not only ensures the security of the AIGC framework but also provides an efficient data analysis and supervision solution for digital platforms.
Decentralized Autonomous Organizations (DAOs) represent a transformative shift in organizational structures, combining decentralized governance with blockchain-based smart contracts. While DAOs present significant opportunities for innovation, they are confronted with several unresolved challenges, such as the centralization of power, the design of effective governance mechanisms, and the legal uncertainties surrounding their operation. Drawing on insights from recent studies and discussions presented in July 2024 at DAWO24, the first European DAO Workshop, this article explores these issues. The purpose of this article is to identify and analyze the critical research streams in DAO studies, particularly in governance mechanisms, technical frameworks, value assessment, and legal dimensions. A systematic approach, following the PRISMA methodology, was employed to analyze contributions from 14 extended abstracts and 11 full papers presented at DAWO24. The findings highlight the need for more equitable governance structures, secure and scalable technical frameworks, standardized tools for assessing DAOs’ value, and coherent legal frameworks to support decentralized operations. The article concludes by outlining future research directions, urging interdisciplinary collaboration to address current gaps and optimize DAO design, operation, and regulation.
The unstoppably increasing number of the Internet of Things (IoT), autonomous agents, and massive distributed web ecosystems have made data acquisition a complicated, risk-prone, and a very sensitive process. Regulation Web data collection is a fixed pipeline that is strictly regulated by established rules and legal limits, and reactive policy audits to operate in traditional forms of governance. Nevertheless, the contemporary digital ecosystem requires a decentralized system of governance that could identify unpredictable streams of data, the shifting web framework, loosely distributed computing individuals, and shifting conditions of regulation. This paper will present Governance-of-Things (GoT), an emerging conceptual and architectural design that will address these issues and show how to smoothly integrate ethical intelligence, regulatory and laws compliance, semantic awareness, and integrity assurance within autonomous systems of web data acquisition. GoT suggests a view where governance follows a first-class computation i.e. embedded, adaptive, intelligent and context-aware. In contrast to traditional approaches of governing IoT, GoT regards any acquisition agent as ethics-regulated, compliance-aware, and self-regulating. Agents do not simply pull information, they negotiate access rights, authenticate provenance, reason about risk, and implement multi-jurisdictional policies all by themselves. The framework combines dynamic enforcement of policies, federated governance, semantic classification pipelines, AI-enhanced agent frameworks built on Java and distributed analytics to create an ecosystem, producing an automated acquisition that is compatible with responsible, transparent, and audit-friendly behaviours. Fairness, legality, transparency, explainability and accountability are the principles of ethical autonomy which are expounded in the paper. GoT has the aspect of federated ethical rule orchestration where the governance layers among organizations in various stakeholders share without necessarily providing the raw information. The system incorporates automation using structural integrity that guarantees cryptographic validation and review trails that are not tampered with. The given adaptive monitoring model promotes the constant policy updating, data flows redirection and the detection of threats. Furthermore, GoT involves semantic intelligence so that data classification, contextual labeling, entity recognition, and domain mapping take place before storing or processing data- therein avoiding compliance violation at its early phases. GoT architecturally has a multi-layer stack that is organized and includes Perception Layer, Autonomous Agent Layer, Governance Core, Distributed Analytics Layer, and Compliance Ledger Layer. The primitives of computational governance are embedded in each layer, making it highly modular and allowing run-time updates of rules and cooperating across agents. Java frameworks boosted with AI facilitate interoperability with legacy enterprise systems and with current base systems. Using experimental simulation, it was found that GoT enhances compliance accuracy, governance throughput, policy adaptation latency and decision explainability on varying scenarios of acquisitions. This article is in the pre-2021 academic style, has extensive literature review, methodological description, architectural schematics, theoretical framework, and profound results discussion. It ends by establishing GoT as an innovative paradigm which is able to influence the future of web data governance, autonomous systems, and distributed analytics
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.
Eduardo V. A. Martins, Eduardo G. Machado, Rafael T. B. Gomes, Wilson S. Melo
Distributed measurement systems (DMS) are crucial for real-time measurements of physical quantities in various fields of industry and engineering, aiming to enhance measurement data reliability. Since technological advancements continue to shape our digital world, the need for secure and consistent data processing in DMS has become increasingly vital. This paper highlights the challenges associated with data integrity and security in DMS and proposes blockchains and Trusted Ex-ecution Environments (TEE) to solve this problem. Blockchain's decentralized and immutable ledger system can enhance data trustworthiness and protect against fraudulent activities, ulti-mately ensuring the reliability and security of critical data in applications involving metering. Complementary, the TEE secure kernel allows smart sensors to sign and send data to the blockchain network without the risk of it being modified. The paper also brings an implementation proposal integrating the Hyperledger Fabric as the blockchain platform and op- TEE operational system on data processing tasks.
In recent years, there has been a significant surge in the popularity of Non-Fungible Tokens (NFTs), and Ethereum has emerged as one of the prominent platforms for the production and trading of these tokens. The primary objective of this research paper is to conduct a thorough analysis of non-fungible tokens (NFTs) based on the Ethereum blockchain. This analysis will be accomplished by utilizing diverse datasets sourced from Kaggle. This paper aims to examine the trends, market dynamics, and factors that exert influence on the ecosystem of non-fungible tokens (NFTs) specifically within the Ethereum blockchain. This paper employs data analysis and visualization techniques to provide valuable insights into the expansion and potential obstacles encountered by the Ethereum non-fungible token (NFT) market. Moreover, it elucidates the influence of this market on the broader cryptocurrency and art sectors.
IOTA 2.0 addresses the dual challenge of dynamic availability and definite finality in distributed ledgers. By combining voting-based and proof-based consensus models, it allows users to strike a balance between these objectives. This innovative approach represents a significant advancement in the field, offering both dynamic availability and definite finality, all within the evolving landscape of distributed ledger technologies.
This paper proposes a framework for efficient remote service support in meta-verse. This work was initiated by a empathy-driven sensitive listening counselor bot enhanced by real and virtual services including digital twins selected by a powerful need/offer matching. This matching is leveraged on ADN (Autonomous Decentralization Network) IBN (Intention Based Network) for efficiency (high performance) exploiting DLT (Distributed Ledger Technology) for safety. This framework makes it possible to provide highly efficient teleservice including telemedicine in virtual spaces, which enables eternal evolution. Moreover, this paper introduces a concept for an integrated learning and evolutionary self-improvement of our metaverse system.
Conor Flynn, Kristin P. Bennett, John Erickson, Aaron Green · 5 authors
With the agile development process of most academic and corporate entities, designing a robust computational back-end system that can support their ever-changing data needs is a constantly evolving challenge. We propose the implementation of a data and language-agnostic system design that handles different data schemes and sources while subsequently providing researchers and developers a way to connect to it that is supported by a vast majority of programming languages. To validate the efficacy of a system with this proposed architecture, we integrate various data sources throughout the decentralized finance (DeFi) space, specifically from DeFi lending protocols, retrieving tens of millions of data points to perform analytics through this system. We then access and process the retrieved data through several different programming languages (R-Lang, Python, and Java). Finally, we analyze the performance of the proposed architecture in relation to other high-performance systems and explore how this system performs under a high computational load.
Web3 and AI have been among the most discussed fields over the recent years, with substantial hype surrounding each field's potential to transform the world as we know it. However, as the hype settles, it's evident that neither AI nor Web3 can address all challenges independently. Consequently, the intersection of AI and Web3 is gaining increased attention, emerging as a new field with the potential to address the limitations of each. In this article, we will focus on the integration of web3 and the AI marketplace, where AI services and products can be provided in a decentralized manner (DeAI). A comprehensive review is provided by summarizing the opportunities and challenges on this topic. Additionally, we offer analyses and solutions to address these challenges. We've developed a framework that lets users pay with any kind of cryptocurrency to get AI services. Additionally, they can also enjoy AI services for free on our platform by simply locking up their assets temporarily in the protocol. This unique approach is a first in the industry. Before this, offering free AI services in the web3 community wasn't possible. Our solution opens up exciting opportunities for the AI marketplace in the web3 space to grow and be widely adopted.
Filip Rezabek, Kilian Glas, Richard von Seck, Achraf Aroua · 6 authors
The recent developments and research in distributed ledger technologies and blockchain have contributed to the increasing adoption of distributed systems. To collect relevant insights into systems' behavior, we observe many evaluation frameworks focusing mainly on the system under test throughput. However, these frameworks often need more comprehensiveness and generality, particularly in adopting a distributed applications' cross-layer approach. This work analyses in detail the requirements for distributed systems assessment. We summarize these findings into a structured methodology and experimentation framework called METHODA. Our approach emphasizes setting up and assessing a broader spectrum of distributed systems and addresses a notable research gap. We showcase the effectiveness of the framework by evaluating four distinct systems and their interaction, leveraging a diverse set of eight carefully selected metrics and 12 essential parameters. Through experimentation and analysis we demonstrate the framework's capabilities to provide valuable insights across various use cases. For instance, we identify that a combination of Trusted Execution Environments with threshold signature scheme FROST introduces minimal overhead on the performance with average latency around \SI{40}{\ms}. We showcase an emulation of realistic systems behavior, e.g., Maximal Extractable Value is possible and could be used to further model such dynamics. The METHODA framework enables a deeper understanding of distributed systems and is a powerful tool for researchers and practitioners navigating the complex landscape of modern computing infrastructures.
Fragmented energy data silos pose a significant obstacle to efficient data management and collaboration in the European energy sector. Creating a unified European energy dataspace aligned with GAIA-X principles (transparency, data security, data protection, interoperability and scalability) is crucial to overcoming this challenge. This paper presents a solution that combines a Distributed Ledger Technology (DLT)-based data marketplace with the International Data Spaces (IDS) Architecture. By utilizing DLT, the proposed marketplace enables secure and transparent tracking of energy datasets within the dataspace. Integration with the IDSA ensures compliance with GAIA-X, promoting interoperability and seamless data exchange. It enables the consolidation of diverse data sources into a unified dataspace, eliminating silos. This fosters efficient and secure data exchange, promotes transparency and trust, and supports collaboration and innovation in the energy sector.
Gautham Narayan, Pavitra Haveri, B H Rashmi, Yashwardhan Deewan
Ensuring secure data provenance is crucial for maintaining accountability and confidentiality in cloud environments. Cloud data provenance involves recording the history of creation and operations performed on cloud data objects. However, establishing trust between cloud customers and service providers remains a challenge, highlighting the need for assured data provenance models in cloud storage. Blockchain technology has emerged as a solution for designing data provenance assurance mechanisms. It provides a decentralized and distributed ledger to record the provenance of digital assets. In this context, we present a blockchain-based framework for ensuring data provenance in cloud storage. Initially, we develop a cloud storage application using OpenStack swift storage. This application caters to the storage needs of university students and faculty while providing data provenance capabilities. Subsequently, we design a data provenance assurance framework for confidential files of users using the Ethereum blockchain. To evaluate the scalability and performance of the proposed framework, we analyze various factors such as transaction throughput, latency, network size, and load on the blockchain network. The performance of the system is compared under two consensus algorithms: Proof of Work and Proof of Authority. By conducting this analysis, we aim to assess the effectiveness and efficiency of the blockchain-based solution in ensuring data provenance in cloud storage environments.
Lucianna Kiffer, Sophia Skorik, Yann Vonlanthen, Roger Wattenhofer
On 15th September 2022, The Merge marked the Ethereum network's transition from computation-hardness-based consensus (proof-of-work) to a committee-based consensus mechanism (proof-of-stake). As a result, all the specialized hardware and GPUs that were being used by miners ceased to be profitable in the main Ethereum network. Miners were then left with the decision of how to re-purpose their hardware. One such choice was to try and make a profit mining another existing PoW system. In this study, we explore this choice by analyzing the hashrate increase in the top PoW networks following the merge. Our findings reveal that the peak increase in hashrate to other PoW networks following The Merge represents an adoption of at least 41% of the hashrate that was present in Ethereum, with 12% remaining more than 5 months later. Though we measure a drastic decrease in profitability by almost an order of magnitude, the continued presence of miners halts claims that power consumption was instantly addressed by Ethereum's switch to PoS.
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 .
Block chain technique is developed from a distributed, dependable application development platform to an irreversible record of crypto currency transaction history. Block chain technology's emergence has sparked a number of possible changes in how corporate operations are managed across numerous industries. However, to the best of research experience, no prior effort has concentrated on integrating block chain to establish a secure and unchangeable data and evidence attribution maintenance architecture that autonomously checks the origination records. Therefore, the research work proficiently introduces the novel Block Chain based Contribute Sequence Tracking (BCCST) system with amalgamation of Trail based Smart Indenture (TSI) and Release Attribution Model (RAM). The study begins by outlining a practical application of the proposed TSI for the disintermediation of commercial activities utilising a notional, shared information ledger. This ledger not only makes monitoring data exchange easier, but it also encourages supply chain participants to cooperate together on a multilateral network. The novel RAM to create immutable data trails that make it easier to gather, maintain, and verify reliable data provenance.
Maintaining accurate provenance records is paramount in digital forensics, as they underpin evidence credibility and integrity, addressing essential aspects like accountability and reproducibility. Blockchains have several properties that can address these requirements. Previous systems utilized public blockchains, i.e., treated blockchain as a black box, and benefiting from the immutability property. However, the blockchain was accessible to everyone, giving rise to security concerns and moreover, efficient extraction of provenance faces challenges due to the enormous scale and complexity of digital data. This necessitates a tailored blockchain design for digital forensics. Our solution, Forensiblock has a novel design that automates investigation steps, ensures secure data access, traces data origins, preserves records, and expedites provenance extraction. Forensiblock incorporates Role-Based Access Control with Staged Authorization (RBAC-SA) and a distributed Merkle root for case tracking. These features support authorized resource access with an efficient retrieval of provenance records. Particularly, comparing two methods for extracting provenance records - off-chain storage retrieval with Merkle root verification and a brute-force search - the off-chain method is significantly better, especially as the blockchain size and number of cases increase. We also found that our distributed Merkle root creation slightly increases smart contract processing time but significantly improves history access. Overall, we show that Forensiblock offers secure, efficient, and reliable handling of digital forensic data.
<ns3:p> <ns3:bold>Background:</ns3:bold> Traditional publishing models, open access and major publishers, cannot adequately address the key challenges of academic publishing today: Speed of peer review, recognition of work and incentive mechanisms, transparency and thrust of the system. </ns3:p> <ns3:p> <ns3:bold>Methods:</ns3:bold> To address these challenges, the authors propose Decentralised Academic Publishing (DAP), which is based on the novel HashNET DLT platform. The DAP introduces several innovative components: tracking the activities of all participants in the peer review process using blockchain and smart contracts, the introduction of the Scholarly Wallet for holding reputation (non-fungible) and reward (fungible) tokens, the use of the Scholarly Wallet as the main interface to the DAP platform, the Virtual Editor that enables automatic discovery of the research area and invitation of reviewers, and finally the global database of evaluated reviewers, ranked by the quality of their previous work. </ns3:p> <ns3:p> <ns3:bold>Results:</ns3:bold> The DAP platform is in the development phase, with the design and functionalities of all modules defined. An exception is the central component of DAP, the Scholarly Wallet module, whose first prototype has already been created, tested and published. The implementation of DAP is planned for the next phase of the HorizonEurope TruBlo project and other research initiatives. The DAP platform will be connected to the publishing ecosystem: 1) as a backend system (distributed blockchain database) for existing publishing platforms and 2) as a standalone publishing platform with its own API interface. </ns3:p> <ns3:p> <ns3:bold>Conclusions:</ns3:bold> The authors believe that DAP has the potential to significantly improve academic peer review and knowledge dissemination. It is expected that the use of blockchain technology, the fast HashNET consensus platform and tokens for reward (fungible) and reputation/ranking (non-fungible) will lead to a more efficient and transparent way of rewarding all participants in the peer review process and ultimately advance scientific research. </ns3:p>
Tomas Bueno Momčilović, Matthias Buchinger, Dian Balta
In its 14 years, distributed ledger technology has attracted increasing attention, investments, enthusiasm, and user base. However, ongoing doubts about its usefulness and recent losses of trust in prominent cryptocurrencies have fueled deeply skeptical assessments. Multiple groups attempted to disentangle the technology from the associated hype and controversy by building workflows for rapid prototyping and informed decision-making, but their mostly isolated work leaves users only with fewer unclarities. To bridge the gaps between these contributions, we develop a holistic analytical framework and open-source web tool for making evidence-based decisions. Consisting of three stages - evaluation, elicitation, and design - the framework relies on input from the users' domain knowledge, maps their choices, and provides an output of needed technology bundles. We apply it to an example clinical use case to clarify the directions of our contribution charts for prototyping, hopefully driving the conversation towards ways to enhance further tools and approaches.