Blockchain Papers

Ahmed Zawia, M.A. Hasan

No abstract is available for this record.

Anhelina Kovach, Jorge Lanza, Leticia Montalvillo, Aitor Urbieta

Securing interoperable and sovereign data exchange in the Industrial Internet of Things (IIoT) for machine data exploitation by third parties presents a significant challenge. This work addresses this by integrating IOTA Distributed Ledger Technology (DLT) with the International Data Spaces (IDS) Reference Architecture Model (RAM), creating a decentralized data space optimized for IIoT ecosystems. This research demonstrates the practical implementation of core IDS architectural concepts within the IOTA framework, overcoming theoretical DLT limitations and showcasing IOTA’s capability to enhance data sovereignty and interoperability in the IIoT, moving beyond traditional blockchains, which are constrained by scalability and efficiency issues. It sets the stage for future evaluations and broader applicability studies, paving the way for advancements in secure, sovereign, interoperable, and efficient data management.

Md. Rafid Haque, Sakibul Islam Munna, Sabbir Ahmed, Md. Tariqul Islam · 6 authors

Centralized version control systems (VCS) are vital for software development but pose risks of data loss and ownership disputes. While blockchain offers a decentralized alternative, existing solutions are often hindered by high latency, compromising the real-time collaboration essential for modern workflows. This study introduces a novel hybrid architecture combining the security of the Ethereum blockchain and the InterPlanetary File System (IPFS) with two key contributions: 1) Shamir's Secret Sharing (SSS) to create a trust-minimized model for key distribution, and 2) an authoritative-first, optimistic-fallback retrieval protocol utilizing a temporary middleware to decouple the user experience from blockchain confirmation delays. We implemented a full prototype and conducted a comprehensive performance evaluation on the public Sepolia testnet. Our results demonstrate that this architecture not only provides a secure, auditable, and resilient platform for source code hosting but also achieves highly competitive user-perceived performance. Our user-perceived push time reduces submission latency by up to 49% compared to a standard git push for common repository sizes, proving that a well-designed decentralized VCS can balance the core tenets of security and decentralization with the practical need for speed and efficiency.

Sathya Krishnasamy, Ilangovan Govindarajan

Web 3.0 represents the next significant evolution of the internet that embodies the underlying decentralized network architectures, distributed ledgers, and advanced AI capabilities. Though the technologies are maturing rapidly, considerable barriers exist to high-scale adoption. The author discusses the barriers and the mitigations through specific technologies maturing to solve those issues in an earlier paper titled Moving Beyond POCs and Pilots, published in 2023 in Blockchain in Healthcare Today. These include privacy-preserving technologies, off-chain and on-chain design optimizations, and the multi-dimensional approach needed in planning and adopting these technologies. As an extension, this paper discusses one such enabler, zero knowledge machine learning (ZKML), which merges two streams of technology in unique ways to address problems in privacy and the cost of inference. Zero-knowledge proofs (ZKP) allow one party to prove the validity of a statement to another party without revealing any additional information about the statement itself. The ZKML combines the cryptographic principle of ZKP with machine learning (ML) techniques. It is still a maturing technology and needs baselines for applications in global healthcare. In this effort, the authors conceptualize the technical and operational feasibility of using ZKML and implement a reference healthcare implementation using the synthetic International Consortium for Health Outcomes Measurement (ICHOM) in the evaluation phase in a global healthcare setting for high-volume data collection, including patient-reported outcomes. Model complexity reduction is researched and reported for the ICHOM diabetes dataset to advance the usage of ML models in global standards of healthcare data collection in network decentralized architectures for increased data protection and efficiencies.

Junchao Chen, Alberto Sonnino, Lefteris Kokoris-Kogias, Mohammad Sadoghi

Sharding has emerged as a critical technique for enhancing blockchain system scalability. However, existing sharding approaches face unique challenges when applied to Directed Acyclic Graph (DAG)-based protocols that integrate expressive smart contract processing. Current solutions predominantly rely on coordination mechanisms like 2PC and require transaction read/write sets to optimize parallel execution. These requirements introduce two fundamental limitations: 1) additional coordination phases incur latency overhead, and 2) pre-declaration of read/write sets proves impractical for Turing-complete smart contracts with dynamic access patterns. This paper presents Thunderbolt, a novel sharding architecture for both single-shard transactions (Single-shard TXs) and cross-shard transactions (Cross-shard TXs) and enables nonblocking reconfiguration to ensure system liveness. Our design introduces 4 key innovations: 1) each replica serves dual roles as a full-shard representative and transaction proposer, employing the Execution-Order-Validation (EOV) model for Single-shard TXs and Order-Execution (OE) model for Cross-shard TXs. 2) we develop a DAG-based coordination protocol that establishes deterministic ordering between two transaction types while preserving concurrent execution capabilities. 3) we implement a dynamic concurrency controller that schedules Single-shard TXs without requiring prior knowledge of read/write sets, enabling runtime dependency resolution. 4) Thunderbolt introduces a nonblocking shard reconfiguration mechanism to address censorship attacks by featuring frequent shard re-assignment without impeding the construction of DAG nor blocking consensus. Thunderbolt achieves a 50x throughput improvement with 64 replicas compared to serial execution in the Tusk framework.

Hao Qin

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.

Dimitris Ntalaperas

This deliverable presents the second iteration of the AI4Gov Decentralised Data Governance (DDG) model, focusing on the implementation of mechanisms that ensure data provenance, reliability, and GDPR-compliant privacy within a decentralized architecture. It details the finalized system design and supporting prototypes that enable transparent data governance and the execution of decentralized business processes through smart contracts, while introducing a redesigned, citizen-centric approach that facilitates participation in open and collaborative governance processes. The document outlines key architectural improvements, including the adoption of decentralized identity frameworks and the integration of Digital Autonomous Organization (DAO) principles for self-governed units. It further discusses the updated technology stack, system functionalities, and relevant regulatory considerations, providing a validated and extensible framework for trustworthy, participatory, and privacy-preserving data governance in AI-driven public-sector applications.

Jonathan Heiss, Fernando Castillo, Xinxin Fan

Decentralized Physical Infrastructure Networks (De-PINS) are secured and governed by blockchains but beyond crypto-economic incentives, they lack measures to establish trust in participating devices and their services. The verification of relevant device credentials during device registration helps to overcome this problem. However, on-chain verification in decentralized applications (dApp) discloses potentially confidential device attributes whereas off-chain verification introduces undesirable trust assumptions. In this paper, we propose a credential-based device registration (CDR) mechanism that verifies device credentials on the blockchain and leverages zero-knowledge proofs (ZKP) to protect confidential device attributes from being disclosed. We characterize CDR for DePINs, present a general system model, and technically evaluate CDR using zkSNARKs with Groth16 [1] and Marlin [2]. Our experiments give first insights into performance impacts and reveal a tradeoff between the applied proof systems.

Rasheed, Yash Chaurasia, Parth Desai, Sujit Gujar

Smart contracts led to the emergence of the decentralized finance (DeFi) marketplace within blockchain ecosystems, where diverse participants engage in financial activities. In traditional finance, there are possibilities to create values, e.g., arbitrage offers to create value from market inefficiencies or front-running offers to extract value for the participants having privileged roles. Such opportunities are readily available -- searching programmatically in DeFi. It is commonly known as Maximal Extractable Value (MEV) in the literature. In this survey, first, we show how lucrative such opportunities can be. Next, we discuss how protocol-following participants trying to capture such opportunities threaten to sabotage blockchain's performance and the core tenets of decentralization, transparency, and trustlessness that blockchains are based on. Then, we explain different attempts by the community in the past to address these issues and the problems introduced by these solutions. Finally, we review the current state of research trying to restore trustlessness and decentralization to provide all DeFi participants with a fair marketplace.

Yuandou Wang, Siamak Farshidi, Sheejan Tripathi, Zhiming Zhao

Context: Scientific research, increasingly reliant on data and computational analysis, confronts the challenge of integrating collaboration and data sharing across disciplines. Collaborative frameworks that support decentralized decision-making and knowledge-sharing are essential, yet integrating them into computational environments presents technical challenges, such as decentralized identity, user-centered policy-making, flexible asset management, automated provenance, and distributed collaborative workflow management. Solution: This study introduces a conceptual framework and its prototype implementation called Decentralized Virtual Research Environment (D-VRE). This approach enhances seamless, trusted data sharing and collaboration within research lifecycles. It incorporates custom sharing policies, secure asset management, collaborative workflows, and research activity tracking, all without centralized oversight. Evaluation: Demonstrated through a real-world case study in the CLARIFY project, the prototype of the decentralized virtual research environment proved effective in enabling advanced data sharing and collaborative scenarios, showcasing its adaptability in scientific research. Results: Integrated into JupyterLab, D-VRE supports custom collaboration agreements and smart contract-based automated execution on the Ethereum blockchain. This ensures secure, verifiable transactions and promotes trust and reliability in shared research findings. Contribution: D-VRE addresses barriers to scientific research collaboration and data sharing, offering a scalable and adaptable decentralized model. This model promotes a more inclusive, efficient, and trustworthy research ecosystem, paving the way for future advancements in virtual research environments.

Arera, Christopher

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.

Yuandou Wang, Sheejan Tripathi, Siamak Farshidi, Zhiming Zhao

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.

P. Subhashini, J Alekhya, Ajay Rana, Sorabh Lakhanpal · 6 authors

Web3 needs complex semantic frameworks to update, validate, and regulate data across decentralized networks as it grows. The whole semantic system of this study includes SV, SC, DOC, ISV, and SG. The Semantic Validation technique performs parametric validation, scoring, and threshold comparison to safeguard data integrity, whereas the Semantic Agreement algorithm combines meanings by adding agreement values. Decentralized ontologies are improved using Distributed Ontology Construction to accommodate new meaning linkages. connected Semantic Verification detects semantic meaning compatibility across linked networks. Lastly, meaning Governance enables individuals to decide on recommended meaning modifications without centralization. Comparative analysis examines the framework. Tables and illustrations indicate its dominance over key aspects. The recommended strategy generally outperforms Web3 evolution methods in security, scale, interoperability, user privacy, and government effectiveness. Visualizing the process with pie charts, layered analysis, and temporal trends shows its efficacy. This semantic framework's consistency, correctness, and flexibility are key responses to Web3's changes. The framework's continual refinement methods can shape Web3 Internet semantics as open networks change. This semantic framework helps establish a decentralized and connected Internet as bitcoin and Web3 technologies progress. It also allows Web3-aligned semantic development.

John Collomosse, Andy Parsons

Provenance facts, such as who made an image and how, can provide valuable context for users to make trust decisions about visual content. Against a backdrop of inexorable progress in generative AI for computer graphics, over two billion people will vote in public elections this year. Emerging standards and provenance enhancing tools promise to play an important role in fighting fake news and the spread of misinformation. In this article, we contrast three provenance enhancing technologies-metadata, fingerprinting, and watermarking-and discuss how we can build upon the complementary strengths of these three pillars to provide robust trust signals to support stories told by real and generative images. Beyond authenticity, we describe how provenance can also underpin new models for value creation in the age of generative AI. In doing so, we address other risks arising with generative AI such as ensuring training consent, and the proper attribution of credit to creatives who contribute their work to train generative models. We show that provenance may be combined with distributed ledger technology to develop novel solutions for recognizing and rewarding creative endeavor in the age of generative AI.

Blake Regalia, Benjamin Adams

The greatest advantage that Web3 applications offer over Web 2.0 is the evolution of the data access layer. Opaque, centralized services that compelled trust from users are replaced by trustless, decentralized systems of smart contracts. However, the public nature of blockchain-based databases, on which smart contracts transact, has typically presented a challenge for applications that depend on data privacy or that rely on participants having incomplete information. This has changed with the introduction of confidential smart contract networks that encrypt the memory state of active contracts as well as their databases stored on-chain. With confidentiality, contracts can more readily implement novel interaction mechanisms that were previously infeasible. Meanwhile, in both Web 2.0 and Web3 applications the user interface continues to play a crucial role in translating user intent into actionable requests. In many cases, developers have shifted intelligence and autonomy into the client-side, leveraging Web technologies for compute, graphics, and networking. Web3's reliance on such frontends has revealed a pain point though, namely that decentralized applications are not accessible to end users without a persistent host serving the application. Here we introduce the Non-Fungible Program (NFP) model for developing self-contained frontend applications that are distributed via blockchain, powered by Web technology, and backed by private databases persisted in encrypted smart contracts. Access to frontend code, as well as backend services, is controlled and guaranteed by smart contracts according to the NFT ownership model, eliminating the need for a separate host. By extension, NFP applications bring interactivity to token owners and enable new functionalities, such as authorization mechanisms for oracles, supplementary Web services, and overlay networks in a secure manner. In addition...

Feng Zhang, Zihao Wang, Ruixin Guo, Guangzhi Qu

Earth observation (EO) data provenance is vital for facilitating data sharing and cooperative processing. However, existing techniques for managing EO data provenance still have various weaknesses, including decentralization, traceability, transparency, tamper-proofing, and security protection. Despite being a transformative solution in various domains, the potential of blockchain technology in EO data provenance remains largely unexplored. This article introduces a blockchain-based solution for EO data provenance, aiming to facilitate data sharing and traceability. We have implemented a prototype based on the blockchain technology and conducted a performance evaluation. To the best of authors' knowledge, this is the first paper to explore the application of blockchain in the management of EO data provenance.

Dani Mertens, Jeha Kim, Jingren Xu, Eunsam Kim · 5 authors

No abstract is available for this record.

Harish Padmanaban

With the widespread integration of artificial intelligence (AI) and blockchain technologies, safeguarding privacy has become of paramount importance. These techniques not only ensure the confidentiality of individuals' data but also maintain the integrity and reliability of information. This study offers an introductory overview of AI and blockchain, highlighting their fusion and the subsequent emergence of privacy protection methodologies. It explores various application contexts, such as data encryption, de-identification, multi-tier distributed ledgers, and k-anonymity techniques. Moreover, the paper critically evaluates five essential dimensions of privacy protection systems within AI-blockchain integration: authorization management, access control, data security, network integrity, and scalability. Additionally, it conducts a comprehensive analysis of existing shortcomings, identifying their root causes and suggesting corresponding remedies. The study categorizes and synthesizes privacy protection methodologies based on AI-blockchain application contexts and technical frameworks. In conclusion, it outlines prospective avenues for the evolution of privacy protection technologies resulting from the integration of AI and blockchain, emphasizing the need to enhance efficiency and security for a more comprehensive safeguarding of privacy.

Yang Xu, Jianbo Shao, Jia Liu, Yulong Shen · 6 authors

Benefiting from the booming of Big Data and artificial intelligence (AI) technologies, data-as-a-service is gradually transforming into knowledge-as-a-service. Extracting knowledge from massive raw data is becoming a popular paradigm to save network resources and improve efficiency, and establishing knowledge markets is receiving increasing attention from academia and industry. In this paper, we propose a one-stop knowledge acquisition ecosystem termed BWKA that covers the whole process from upper-layer knowledge trading to underlying knowledge generation. In the knowledge trading process, the knowledge-as-a-service platform (KSP) is the buyer and publishes knowledge demands to multiple local knowledge sellers (LKSs). In the knowledge generation process, each LKS aggregates data from its sensors and then trains data into knowledge according to the KSP's requirements. We resort to blockchain technology and provide a series of tailored operating rules and functions to protect the truthfulness of data gathering and the fairness of knowledge trading. In addition, we introduce incentive mechanisms to stimulate selfish and rational entities in the BWKA ecosystem to participate in knowledge acquisition. To analyze the strategic interactions among entities theoretically, we develop a nested hierarchical game model, where the upper-layer knowledge trading is evaluated based on the Contract Theory, and the lower-layer knowledge generation is formulated as a two-stage Stackelberg game. By solving the nested hierarchical game in a backward inductive way, we identify the optimal strategy for each entity in closed form. Experiments on the Ethereum blockchain and simulation results demonstrate the practical operability and outstanding performance of the BWKA ecosystem.

Alper Alimoğlu, Can Özturan

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.

Mojtaba Eshghie, Mikael Jafari, Cyrille Artho

Decentralized Finance (DeFi) systems leverage blockchain oracles to access off/on-chain data as a service. Therefore, maintaining the integrity of oracle data is essential. However, the integrity of these oracles data can be compromised through different attacks, and the effectiveness of these attacks varies depending on the specific stage of the oracle's lifecycle. This work presents a comprehensive analysis of this lifecycle, identifying potential attack types and examining the efficacy of existing defense mechanisms. We propose a generalized model encompassing data creation, submission, consensus, election, and deprecation stages. We evaluate our model against seven recent high-profile DeFi exploits totaling $187 million. We have also studied bond systems as a preventive measure against at least a subset of oracle exploits. Our findings suggest that while bond systems increase the cost of attacks, thereby fortifying oracle data integrity against adversarial manipulations, they also require careful calibration to avoid hindering honest participation.

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.

Ayesha Imran Malik, Prof. John Smith

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

Georgios Gkogkos, Nikolaos Giakoumoglou, Eleftheria Maria Pechlivani, Konstantinos Votis · 5 authors

The integration of Artificial Intelligence (AI) and Distributed Ledger Technology (DLT) into Decision Support Systems (DSS) is revolutionizing agriculture, enabling data-driven decision-making and ensuring the integrity of AI model results for enhanced productivity. To ensure the reliability of AI-driven insights, a pioneering approach is proposed, which employs Distributed Ledger Technology (DLT). The proposed system combines advanced AI algorithms with the security and transparency of DLT. By leveraging digital signatures, cryptographic hashing, and timestamping, this solution guarantees the immutability of data recorded in the ledger. This innovation fosters stakeholder trust, enabling independent verification of AI model outputs by policymakers, researchers, and farmers. The system’s accountability and transparency make it a valuable tool for promoting data interoperability and collaboration across diverse agricultural systems. This study outlines the system’s architecture, testing, and assessment, highlighting its role in preserving data integrity and ensuring accurate AI model outputs. This technology has the potential to revolutionize decision-making in AI-driven agriculture, addressing critical concerns around data reliability and promoting more efficient and sustainable practices.