Abstract Blockchain infrastructures have emerged as a disruptive technology and have led to the realization of cryptocurrencies (peer‐to‐peer payment systems) and smart contracts. They can have a wide range of application areas in e‐Science due to their open, public nature and global accessability in a trustless manner. We propose and implement a smart contract called eBlocBroker, which is an autonomous blockchain‐based middleware system for volunteer computing and providing data resources for e‐Science. The eBlocBroker infrastructure connects requesters who need to combine applications (jobs) with datasets and run them via an Ethereum‐based private blockchain network (Bloxberg) on providers that utilize computational and data resources on clouds or home servers. It uses cloud storage, such as B2DROP, IPFS, or Google Drive, to store and transfer data between requesters and providers. Each provider utilizes the Slurm workload manager to execute jobs submitted through eBlocBroker. In this paper, we demonstrate how an autonomous organization programmed as a smart contract can be used to deploy a marketplace that supports data and computation‐intensive research projects. We propose a cost model implemented as a function in the smart contract which calculates and records computation and dataset usage costs. We develop a Python‐based system to communicate with eBlocBroker and orchestrate jobs' execution on the provider's end. We present eBlocBroker's features, infrastructure, implementation, algorithms and experimental results.
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.
Data provenance is critical for establishing the origin, authenticity, and integrity of data in distributed environments. Traditional provenance systems often face challenges such as tampering, lack of transparency, and centralized trust issues. Blockchain technology, with its immutable ledger and decentralized consensus mechanisms, provides a promising solution to these challenges. This article explores blockchain-based cryptographic methods that enhance the security and reliability of data provenance systems. We discuss key cryptographic primitives such as digital signatures, hash chains, and zero-knowledge proofs integrated within blockchain frameworks to guarantee secure and verifiable provenance records. The study also highlights practical implementations and identifies open challenges for future research
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.
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.
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.
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.
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.
Zero-knowledge proof (ZKP) frameworks have the potential to revolutionize the handling of sensitive data in various domains. However, deploying ZKP frameworks with real-world data presents several challenges, including scalability, usability, and interoperability. In this project, we present Fact Fortress, an end-to-end framework for designing and deploying zero-knowledge proofs of general statements. Our solution leverages proofs of data provenance and auditable data access policies to ensure the trustworthiness of how sensitive data is handled and provide assurance of the computations that have been performed on it. ZKP is mostly associated with blockchain technology, where it enhances transaction privacy and scalability through rollups, addressing the data inherent to the blockchain. Our approach focuses on safeguarding the privacy of data external to the blockchain, with the blockchain serving as publicly auditable infrastructure to verify the validity of ZK proofs and track how data access has been granted without revealing the data itself. Additionally, our framework provides high-level abstractions that enable developers to express complex computations without worrying about the underlying arithmetic circuits and facilitates the deployment of on-chain verifiers. Although our approach demonstrated fair scalability for large datasets, there is still room for improvement, and further work is needed to enhance its scalability. By enabling on-chain verification of computation and data provenance without revealing any information about the data itself, our solution ensures the integrity of the computations on the data while preserving its privacy.
Blockchain has received attention for its potential use in business. Bitcoin is powered by blockchain, and interest in it has surged in the past few years. It has many uses that need to be modeled. Modeling is used in many walks of life to share ideas, reduce complexity, achieve close alignment of one person viewpoint with another and provide abstractions of a system at some level of precision and detail. Software modeling is used in Model Driven Engineering (MDE), and Domain Specific Languages (DSLs) ease model development and provide intuitive syntax for domain experts. The present study has designed and evaluated a meta-model for the bitcoin application domain to facilitate application development and help in truly understanding bitcoin. The proposed meta-model, including stereotypes, tagged values, enumerations and a set of constraints defined by Object Constraint Language (OCL), was defined as a Unified Modeling Language (UML) profile and was implemented in the Sparx Enterprise Architect (Sparx EA) modeling tool. A case study developed by our meta-model is also presented.