Ensuring the reproducibility of scientific simulations is a persistent challenge, despite current best practices like version control and containerization. Factors such as floating-point arithmetic variations, hardware differences, and concurrency issues often prevent bit-for-bit replication of results. This paper investigates the techniques that distributed ledger technologies employ to achieve deterministic computations and application of these techniques to enhance the reproducibility, trustworthiness and verifiability of scientific simulations. We explore two primary approaches: executing simulations directly âon-chainâ for complete transparency and deterministic replay, and performing computations âoff-chainâ while anchoring their integrity to a blockchain via cryptographic proofs, such as Zero-Knowledge Proofs (ZKPs) and Merkle trees.
Introduction Decentralized autonomous organizations in decentralized science face unique organizational and scientific demands. This study examines core challenges encountered by DeSci DAOs and how these challenges affect governance and research practice. Methods Ten semi-structured interviews were conducted with coâfounders, workingâgroup leads, and longâterm contributors. Transcripts were analyzed using Kuckartzâs sixâphase qualitative content analysis. Categories were developed and refined to synthesize recurrent themes across interviews. Results Nineteen sub-categories clustered into six domains: governance, financials, contribution, onboarding, operations, and science. Findings highlight tensions between tokenâweighted decision making and domain expertise, laborâintensive hybrid accounting practices, persistent talent shortages, steep Web3 onboarding curves, fragmented project coordination, and scienceâspecific issues that include negotiations with technology transfer offices and the tokenization of research assets. The resulting category system provides a diagnostic baseline for understanding how decentralized governance intersects with scientific rigor. Discussion DeSci DAOs progress most effectively when blockchain-enabled transparency is paired with clearly defined coordination roles, structured onboarding pathways, and credible mechanisms for scientific validation. These features help balance organizational experimentation with proven practices and support more reliable scientific workflows.
As generative AI (GenAI) technologies proliferate, ensuring trust and transparency in digital ecosystems becomes increasingly critical, particularly within democratic frameworks. This article examines decentralized Web3 mechanisms—blockchain, decentralized autonomous organizations (DAOs), and data cooperatives—as foundational tools for enhancing trust in GenAI. These mechanisms are analyzed within the framework of the EU’s AI Act and the Draghi Report, focusing on their potential to support content authenticity, community-driven verification, and data sovereignty. Based on a systematic policy analysis, this article proposes a multi-layered framework to mitigate the risks of AI-generated misinformation. Specifically, as a result of this analysis, it identifies and evaluates seven detection techniques of trust stemming from the action research conducted in the Horizon Europe lighthouse project called Enfield: (i) federated learning for decentralized AI detection, (ii) blockchain-based provenance tracking, (iii) Zero-Knowledge Proofs for content authentication, (iv) DAOs for crowdsourced verification, (v) AI-powered digital watermarking, (vi) explainable AI (XAI) for content detection, and (vii) Privacy-Preserving Machine Learning (PPML). By leveraging these approaches, the framework strengthens AI governance through peer-to-peer (P2P) structures while addressing the socio-political challenges of AI-driven misinformation. Ultimately, this research contributes to the development of resilient democratic systems in an era of increasing technopolitical polarization.
This research proposes a Blockchain-driven solution for enhancing the integrity and security of clinical trials, introducing a specialized system called Blockchain for Securing Clinical Trials (BC-SCT). The system reimagines traditional clinical trial data management by offering a decentralized, tamper-resistant platform that ensures trust, transparency, and efficiency across stakeholders including researchers, sponsors, and regulatory bodies.BC-SCT employs modern consensus mechanisms such as Proof-of-Authority (PoA) and Delegated Proof of Stake (DPoS) to significantly reduce transaction processing delaysâfrom 900 ms to 550 ms across 50 transactionsâensuring faster data validation without compromising reliability. It also demonstrates strong performance under simultaneous query loads, cutting response times from 70 ms to 40 ms, a 43% improvement in real-time data access. To handle the scale and complexity of clinical data, the system incorporates features like data sharding, in-memory caching, and off-chain storage. These enhancements reduce Blockchain ledger load by 20%, lowering storage requirements from 100 GB to 80 GB for 10,000 entriesâwhile maintaining high-speed access and data fidelity. Through these innovations, BC-SCT offers a future-proof foundation for conducting and overseeing clinical trials, addressing long-standing issues related to data manipulation, inefficiency, and lack of transparency in research workflows.
In response to the growing demand for enhanced performance and power efficiency, the semiconductor industry has witnessed a paradigm shift toward heterogeneous integration, giving rise to 2.5D/3D chips. These chips incorporate diverse chiplets, manufactured globally and integrated into a single chip. Securing these complex 2.5D/3D integrated circuits (ICs) presents a formidable challenge due to inherent trust issues within the semiconductor supply chain. Chiplets produced in untrusted locations may be susceptible to tampering, introducing malicious circuits that could compromise sensitive information. This paper introduces an innovative approach that leverages blockchain technology to establish traceability for ICs and chiplets throughout the supply chain. Given that chiplet manufacturers are dispersed globally and may operate within different blockchain consortiums, ensuring the integrity of data within each blockchain ledger becomes imperative. To address this, we propose a novel dual-layer approach for establishing distributed trust across diverse blockchain ledgers. The lower layer comprises of a blockchain-based framework for IC supply chain provenance that enables transactions between blockchain instances run by different consortiums, making it possible to trace the complete provenance DAG of each IC. The upper layer implements a multi-chain reputation scheme that assigns reputation scores to entities while specifically accounting for high-risk transactions that cross blockchain trust zones. This approach enhances the credibility of the blockchain data, mitigating potential risks associated with the use of multiple consortiums and ensuring a robust foundation for securing 2.5D/3D ICs in the evolving landscape of heterogeneous integration.
The rapid evolution of Ethereumâs infrastructure calls for innovative mechanisms to enhance scalability, security, and performance. This paper introduces BeamSNARKS, a cutting-edge framework designed to address critical challenges in zero-knowledge proof systems. BeamSNARKS encompasses two groundbreaking innovations: the Dynamic zkSNARKS Generation Optimization Mechanism and the Dynamic SNARKification Technology. The former revolutionizes computational efficiency by dynamically retrieving state data relevant to proof generation, minimizing bandwidth and storage requirements while maintaining validation accuracy. The latter introduces adaptive circuit design and hierarchical proof aggregation to optimize transaction throughput and reduce the computational and financial overhead of Layer 1 submissions. Together, these innovations establish BeamSNARKS as a pivotal advancement in scalable, efficient, and resource-optimized zero-knowledge proof systems. Through comprehensive analysis and targeted experiments, this paper evaluates the performance of BeamSNARKSâs innovations, demonstrating their potential to transform Ethereumâs decentralized ecosystem and lay the groundwork for future high-throughput applications.
This work presents the DGChain (Data-Git- for Blockchain) project. This Python package allows version control of data in blockchain and IPFS based on a DAO (decentralized autonomous organization) for managing data in the development cycles of reproducible computational scientific research. Analyzes the benefits of using this Blockchain-Based Decentralized Architecture to mediate collaborative interactions between developers compared to existing solutions. Presents a use case in developing a medical research project and typical IRIS example to offer the traceability of changes and provenance of metadata, data, and code in Data / Software Version Control systems through management of intrinsic hash-based persistent, immutable CIDs (Content Identifier) recorded in Merkle trees in the development cycle of its main products, publication, software source code, and Data to guarantee reproducibility and trustworthiness in computational scientific research using DGChain.
In database applications involving sensitive data, the dual imperatives of data confidentiality and provable (verifiable) query processing are important. This paper introduces PoneglyphDB, a database system that leverages non-interactive zero-knowledge proofs (ZKP) to support both confidentiality and provability. Unlike traditional databases, PoneglyphDB enhances confidentiality by ensuring that raw data remains exclusively with the host, while also enabling verifying the correctness of query responses by providing proofs to clients. The main innovation in this paper is proposing efficient ZKP designs (called circuits) for basic operations in SQL query processing. These basic operation circuits are then combined to form ZKP circuits for larger, more complex queries. PoneglyphDB's circuits are carefully designed to be efficient by utilizing advances in cryptography such as PLONKish-based circuits, recursive proof composition techniques, and designing with low-order polynomial constraints. We demonstrate the performance of PoneglyphDB with the standard TPC-H benchmark. Our experimental results show that PoneglyphDB can efficiently achieve both confidentiality and provability, outperforming existing state-of-the-art ZKP methods.
Jan Lauinger, Jens Ernstberger, Andreas Finkenzeller, Sebastian Steinhorst
Web users can gather data from secure endpoints and demonstrate the provenance of sensitive data to any third party by using privacy-preserving TLS oracles. In practice, privacy-preserving TLS oracles remain limited and cannot verify larger, sensitive data sets. In this work, we introduce new optimizations for TLS oracles, which enhance the efficiency of selectively verifying the provenance of confidential web data. The novelty of our work is a construction which secures an honest verifier zero-knowledge proof system in the asymmetric privacy setting while retaining security against malicious adversaries. Concerning TLS 1.3 in the one round-trip time (1-RTT) mode, we propose a new, optimized garble-then-prove paradigm in a security setting with malicious adversaries. Our improvements reach new performance benchmarks and facilitate a practical deployment of privacy-preserving TLS oracles in web browsers.
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.
Tarek Zaarour, Ahmed Khalid, Preeja Pradeep, Ahmed H. Zahran
Knowledge graphs have proven vital for efficient data management, enhanced search capabilities, and improved decision-making in various information technology domains. However, constructing reliable knowledge graphs in decentralized ecosystems, with distributed autonomous actors, poses significant challenges related to asynchronous transmission, out-of-order knowledge-sharing, device heterogeneity, and trust issues. These challenges are also present in resource orchestration within multi-cloud edge ecosystems where multiple stakeholders must collaborate and share information to enable next-gen smart applications. In this paper, we propose a novel system design that utilizes Distributed Ledger Technology to build knowledge graphs. This approach ensures consistent and trustworthy knowledge sharing among orchestrators in a cloud-edge continuum. Our solution accommodates diverse requirements of both cloud and edge servers, allowing clients to construct complete historic graphs or build filtered sub-graphs. We deploy our solution in a multi-cloud edge environment and construct knowledge graphs representing the system state, including clusters, servers, microservices, and various resources. We validate the feasibility and performance of our solution through a real-world deployment and experiments in a smart shopping use case. Results demonstrate that the proposed solution achieves the claimed benefits with minimal or acceptable delays in comparison to traditional event streaming services.
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 IOTAâs 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.
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.
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 modelâs 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.
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.
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.
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.
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.
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.
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...
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.