In complex environments such as those incorporating distributed and edge computing, middleware plays a critical role in meeting the communication and performance requirements of distributed systems by providing communication flow and integration capabilities. Its inherent advantages, such as abstraction of complexities, enhanced interoperability and scalability, make it ideal for managing tasks such as federated learning in edge AI environments. In addition, by supporting secure and energy-efficient operations, the middleware fosters sustainability, enabling green blockchain solutions and low-power distributed ledger technologies (DLTs) to thrive for managing dynamic ecosystems such as dAIEDGE. This deliverable D5.3, "Middleware prototype" presents the first version of dAIEDGE middleware. This work has been developed during the first year of dAIEDGE project from M4 to M16. In general, the document outlines the first version of the middleware developed collaboratively with task partners, by the University of Salamanca (USAL) as part of Task T5.2, "Middleware and Networks for Edge AI," within the dAIEDGE project. This task reflects a joint effort involving multiple participants, including BCA, BTH, CETIC, KUL, VICOM, and UEDIN.
Esther Uzoka, Bisola Akeju, Olumide Kumuyi, David Excel Ozowara
The Framework for Data Governance and Compliance Across Distributed Multicloud Infrastructures provides a comprehensive model for managing data integrity, privacy, and regulatory alignment in increasingly complex hybrid and multicloud environments. As organizations adopt distributed computing to enhance scalability, resilience, and performance, they face significant challenges in maintaining consistent governance across heterogeneous platforms operated by multiple providers. This framework establishes a unified governance architecture that integrates policy-based orchestration, automated compliance auditing, and federated identity management to ensure data sovereignty, accountability, and interoperability across diverse cloud ecosystems.At its core, the framework emphasizes data classification, lifecycle management, and access control standardization. Sensitive data are categorized by regulatory requirement and security level, while dynamic policies enforce encryption, anonymization, and retention protocols in accordance with frameworks such as GDPR, HIPAA, and ISO 27001. By leveraging federated metadata catalogs and distributed ledgers, the system enables traceable data provenance and immutable audit trails across hybrid environments. A zero-trust security paradigm further ensures that all access requests are continuously verified, regardless of origin, thereby mitigating insider threats and cross-cloud vulnerabilities.The framework also integrates AI-driven compliance monitoring to detect policy violations, automate reporting, and support adaptive governance in real time. Through interoperable APIs and compliance-as-code implementations, organizations can harmonize data policies across public, private, and edge cloud resources while maintaining jurisdictional and contractual adherence.In promoting transparency and resilience, this framework underscores the importance of cross-sector collaboration among regulators, cloud providers, and enterprises. By unifying governance, security, and compliance strategies, it advances a scalable model for secure data management in distributed infrastructuresenabling innovation, regulatory trust, and sustainable digital transformation in the multicloud era.
Hasan Akgul, Daniel Borg, Arta Berisha, Amina Rahimova · 6 authors
Large language models are often adapted through parameter efficient fine tuning, but current release practices provide weak assurances about what data were used and how updates were computed. We present Verifiable Fine Tuning, a protocol and system that produces succinct zero knowledge proofs that a released model was obtained from a public initialization under a declared training program and an auditable dataset commitment. The approach combines five elements. First, commitments that bind data sources, preprocessing, licenses, and per epoch quota counters to a manifest. Second, a verifiable sampler that supports public replayable and private index hiding batch selection. Third, update circuits restricted to parameter efficient fine tuning that enforce AdamW style optimizer semantics and proof friendly approximations with explicit error budgets. Fourth, recursive aggregation that folds per step proofs into per epoch and end to end certificates with millisecond verification. Fifth, provenance binding and optional trusted execution property cards that attest code identity and constants. On English and bilingual instruction mixtures, the method maintains utility within tight budgets while achieving practical proof performance. Policy quotas are enforced with zero violations, and private sampling windows show no measurable index leakage. Federated experiments demonstrate that the system composes with probabilistic audits and bandwidth constraints. These results indicate that end to end verifiable fine tuning is feasible today for real parameter efficient pipelines, closing a critical trust gap for regulated and decentralized deployments.
George Chidera Akor, Love Allen Chijioke Ahakonye, Jae Min Lee, D. Kim
The increasing use of AI-powered tools for smart contract security audits presents a critical challenge: ensuring the integrity and provenance of audit reports. Traditional methods lack cryptographic guarantees linking audit outputs to specific AI models and source code, creating vulnerabilities to tampering and misattribution. To address this, we propose ProvAuditChain, a gas-efficient, hybrid on-chain/off-chain framework that records immutable provenance of AI-driven audit reports on Layer 2 blockchain networks. ProvAuditChain utilizes lightweight smart contracts to securely anchor cryptographically signed audit report hashes on-chain, while storing the complete reports on decentralized IPFS storage. We deploy the system on the Arbitrum Sepolia testnet and benchmark gas consumption, latency, and throughput across 600 audit cycles. Our results demonstrate a stable average gas cost of approximately 173,000 per audit, equivalent to roughly $0.05 USD, alongside a throughput of nearly six audits per minute. These findings confirm the practical viability of ProvAuditChain for integration into automated CI/CD pipelines, providing a scalable foundation for trustworthy AI accountability in decentralized ecosystems.
Distributed-ledger technologies (DLTs) have upended the design logic of, data-sharing web architectures, especially within sectors that demand uncompromising transparency, indelible audit trails, and decentralised governance. Yet curating an optimal DLT stack remains an intricate optimisation puzzle involving nuanced trade-offs across cryptographic rigour, elastic scalability, experiential ergonomics, propagation latency, cross-ledger interoperability, and fiscal prudence. To navigate this complexity, we introduce a tiered decision-support framework that welds expert-elicited priorities to empirical performance signals within a rigorous multi-criteria outranking model. The scheme yields transparent, rank-ordered shortlists of candidate ledgers and is demonstrated across healthcare, fintech, and supply-chain provenance scenarios. Results confirm the model’s ability to surface context-specific “best fits” even when decision objectives clash, thereby equipping engineers, CIOs, and policy designers with a defensible roadmap for trustworthy, efficient, and governance-aligned blockchain adoption. Future iterations will embed fuzzy logic and live-telemetry feedback to sharpen responsiveness in rapidly evolving operating environments.
As blockchain systems grow in complexity, secure and efficient smart contract development remains a crucial challenge. Large Language Models (LLMs) like DeepSeek promise significant enhancements in developer productivity through automated code generation, debugging, and testing. This study focuses on Solidity, the dominant language for Ethereum smart contracts, where correctness, gas efficiency, and security are critical to real-world adoption. This study evaluates the capabilities of DeepSeek’s V3 and R1 models, a non-reasoning Mixture-of-Experts architecture and a reasoning-based model trained via reinforcement learning, respectively, in automating Solidity contract generation and testing, as well as identifying and fixing common vulnerabilities. We designed a controlled experimental framework to evaluate both models by generating and analysing a diverse set of smart contracts, including standardised tokens (ERC20, ERC721, ERC1155) and real-world application scenarios (Supply Chain, Token Exchange, Auction). The evaluation is grounded on a multidimensional metric suite covering quality, technical robustness and process characteristics. Vulnerability detection and patching capabilities are tested using predefined vulnerable contracts and guided patch prompts. The analysis spans six levels of prompt complexity and compares the impact of reasoning-based and non-reasoning-based generation strategies. Findings reveal that R1 delivers more accurate and optimised outputs under high complexity, while V3 performs more consistently in simpler tasks with simpler code structures. However, both models exhibit persistent hallucinations, limitations in vulnerability coverage, and inconsistencies due to prompt formulation. The correlation between re-evaluation patterns and output quality suggests that reasoning helps in complex scenarios, although excessive revisions may lead to over-engineered or unstable solutions. Neither model is robust enough to autonomously generate issue-free smart contracts in complex or security-critical scenarios, underscoring the need for human oversight. These findings highlight best practices for integrating LLMs into blockchain development workflows and emphasise the importance of aligning model selection with task complexity and security requirements.
This paper addresses one of the most noteworthy issues in the recent virtual asset market, the privacy concerns related to token transactions of Real-World Assets tokens, known as RWA tokens. Following the advent of Bitcoin, the virtual asset market has experienced explosive growth, spawning movements to link real-world assets with virtual assets. However, due to the transparency principle of blockchain technology, the anonymity of traders cannot be guaranteed. In the existing blockchain environment, there have been instances of protecting the privacy of fungible tokens (FTs) using mixer services. Moreover, numerous studies have been conducted to secure the privacy of non-fungible tokens (NFTs). However, due to the unique characteristics of RWA tokens and the limitations of each study, it has been challenging to achieve the goal of anonymity protection effectively. This paper proposes a new token trading platform, the ARTeX, designed to resolve these issues. This platform not only addresses the shortcomings of existing methods but also ensures the anonymity of traders while enhancing safeguards against illegal activities.
Abstract Artificial intelligence (AI) systems are rapidly approaching capabilities that require an increasing level of human control. Existing AI alignment techniques remain opaque, model-specific, and vulnerable in human-level AI, or post-quantum scenarios. To address these issues, this paper proposes a novel AI alignment system architecture in which AI alignment rules are encoded as immutable smart contracts on a blockchain. The blockchain, in turn, is governed by a Proof of Personhood (PoP) consensus mechanism that only admits human agents to the rule validation processes. To protect the privacy of human agents in the identity verification process, the proposed AI alignment system facilitates techniques such as key derivation functions and asymmetric encryption of biometric data. In addition, this system also utilizes blockchain-based decentralized identity (DID) and zero-knowledge proofs (ZKPs). To ensure privacy in post-quantum scenarios, biometric data are linked to zk-STARKs. The proposed AI alignment system is formally described to capture human and AI agents, verification, authentication, and Sybil resistance. The AI shield, a reactive system that prevents unsafe actions by an AI agent that would violate predetermined conditions, enforces the blockchain-based AI alignment rules in real-time, independently of the underlying AI model. Thus, the contribution of this paper is a conceptual framework for the implementation of blockchain technology that utilizes a PoP-based consensus mechanism and zk-STARKs to foster privacy-friendly societal involvement and public auditability of AI developments, providing a democratically governed AI alignment layer applicable to current and future AI models, including those in a post-quantum era.
Edgar Roberto Dulce Villarreal, Julio Ariel Hurtado Alegría, José García-Alonso
Interoperability between blockchain platforms remains a key challenge, particularly in sensitive domains such as healthcare, where the secure and consistent exchange of clinical information between institutions is essential. While technical interoperability solutions exist, semantic interoperability at the level of smart contracts continues to be a significant limitation. This paper presents MUISCA, a mechanism based on Model-Driven Engineering that enables the automatic generation of interoperable smart contracts across different blockchain platforms. By defining metamodels, abstract models, and transformation rules, MUISCA produces platform-specific code for technologies such as Ethereum and Hyperledger Fabric. The mechanism was validated through a healthcare case study focused on patient transfers between medical institutions, demonstrating its ability to support the secure exchange of clinical data. Additionally, its acceptance was evaluated through expert surveys assessing perceived usefulness and ease of use. Results show that MUISCA improves smart contract portability, reduces implementation errors, and enhances system security. The proposed solution contributes to advancing semantic interoperability in blockchain-based health information systems and provides a foundation for broader application in other critical domains that require high levels of integration and data protection.
The digital forensic investigation process overwhelmingly depends on the unbroken, tamper-proof, and audit able Chain of Custody for evidence data. However, most traditional Chain of Custody systems suffer limitations being either static permission control, weak traceability, or even worse lack implementation of cryptographically enforced privacy and integrity guarantees in evidence lifecycle management. These failures can disable real-time, transparent, and secure evidence life cycle management, especially in costly, heterogeneous, and multi-party environments. The work introduces FAIR-CoC, a Forensic Adaptive Integrity and Reputation Chain-of-Custody system conceptually based on the Hybrid Blockchain-IPFS CoC Ledger (HBI-CoC) architecture to resolve these problems. This system uses IPFS as a decentralized repository for forensic artifacts while using private Ethereum blockchain for immutable record keeping of cryptographic hashes, access metadata, and smart contract logic. Furthermore, the framework consists of five key parts, ZK-TIV (Zero-Knowledge Temporal Integrity Verifier) enables evidence access within permissible timestamp windows using zk-SNARK proofs without revealing accessor identities to enhance privacy and temporal accountability in the process; the MV-PGChain (MultiVector Provenance Graph Chain) builds high fidelity provenance graph capturing handler, location, tool, and timestamp changes, with Merkle root snap-shots anchored on-chains; AWReS (Access Weighted Reputation Scorer) dynamically assesses trustworthiness for custodians using on-chain behavioral analytics; HAT-FSS (Homomorphic Audit Tags for Forensic Shard Storage) allows encrypted auditability for IPFS-stored shards using homomorphic verification tags; PA-ESC (Predictive Access Escalation Smart Contracts) embeds AI-based access behavior modeling to automate privilege revocation or escalations. Collectively, these functionalities present a novel adaptive and privacy-preserving CoC framework with solid integrity, traceability, and trust guarantees. Experimental evaluations contend with low latency and accuracy whether across all modules, establishing FAIR-CoC as a leap forward toward secure, scalable, and intelligent forensic chain-ofcustody systems.
Scientific knowledge production is undergoing a dual transformation. On one front, Decentralized Science (DeSci) leverages blockchain-based infrastructures to reconfigure how research is funded, verified, and governed, disintermediating legacy gatekeepers through tokenized incentives and distributed provenance. On the other, Artificial Intelligence (AI) is automating core dimensions of science, from hypothesis generation to experimental execution and model validation. This paper introduces DeScAI, a theoretical framework that unifies these domains into a recursive, self-verifying epistemic system governed by autonomous agents operating within decentralized, trust-minimized networks. We present a five-stratum architecture for DeScAI, hypothesizing that its integration enables epistemic acceleration, pluralistic inquiry, and cryptographically auditable trust. Methods include a structured literature synthesis (2018–2025), conceptual modeling, and descriptive analysis of 14 projects. Three hypothetical trajectories for future empirical investigation are proposed concerning cycle-time compression, epistemic pluralism, and reproducibility amplification. We conclude that DeScAI is not speculative: its core components are already deployed. What remains is orchestration, stitching together decentralized ledgers, incentive protocols, self-sovereign scientific agents (SSA), and cryptographic infrastructures into a single, recursive system. If successful, DeScAI could radically reduce the latency between hypothesis and verification, reconfigure scientific legitimacy as a live, contestable signal, and transform the incentive structure of research itself.
Marine carbon dioxide removal (mCDR) projects are increasingly recognized as a strategic pillar in climate change mitigation. However, their effectiveness and credibility critically depend on the ability to implement secure and verifiable Monitoring, Reporting, and Verification (MRV) procedures, particularly in remote, adversarial, and resource-constrained environments like underwater ecosystems. Despite growing interest in mCDR, current MRV frameworks remain inadequate for such challenging contexts, lacking essential mechanisms to ensure secure device identity, reliable data provenance, and verifiable auditability, as mandated by standards such as ISO 14064-2 and ISO 14064-3. This thesis addresses these limitations by first focusing on the fundamental security challenges that arise in underwater untrusted environments. To overcome these barriers, it introduces a set of modular, composable building blocks that integrate: i) decentralized identifiers (DIDs) for self-sovereign identity management, ii) physically unclonable functions (PUFs) to cryptographically bind secrets to hardwaredevices, iii) non-interactive zero-knowledge proofs (NIZKPs) to enable lightweight and privacy-preserving authentication, and iv) distributed ledger technologies (DLTs) to ensure immutable and verifiable data anchoring. These solutions are designed to be modular, interoperable, and composable, providing the necessary foundation to build secure, transparent, and standards-compliant MRV frameworks suitable for deployment in underwater and blue carbon ecosystems. Crucially, the proposed work does not merely adapt to MRV requirements but proactively resolves critical security gaps that existing MRV models overlook, thus enabling trustworthy data collection, secure identity management, and verifiable certification in these complex environments. The individual building blocks have been implemented and validated on constrained embedded platforms (e.g., ESP32-C3), demonstrating their feasibility under the constraints of underwater sensing. Formal security guarantees are established using symbolic analysis, while empirical evaluations quantify the computational and communication overhead of each component under realistic conditions. By starting from low-level cryptographic primitives and addressing core security challenges, this thesis delivers a foundational contribution to the development of secure, verifiable, and scalable MRV infrastructures for underwater and blue carbon applications. The proposed approach supports the creation of trustworthy, standards-aligned MRV frameworks, enabling verifiable environmental accountability even in hostile and resource-constrained deployment scenarios.
Carlo Beltracchi, Ahmed Elmaraghy, Pierpaolo Ruttico, S. Maccagnan
This contribution proposes a way to broaden access to computational design by combining: (1) an agentic workflow where AI micro-agents translate natural-language prompts into executable, self-verified parametric graphs; (2) a data-driven economy in which each reuse of logic triggers automatic micropayments; and (3) a decentralised network that stores versions, rights and transactions on-chain. Assessor, provider and validator agents assemble, check and publish sub-graphs serialised as semi-fungible tokens; a blockchain ledger tracks lineage and redistributes royalties. The platform merges open-source principles with Web3 incentives: newcomers gain ready-to-use solutions, experienced designers monetise know-how, and the community governs parameters via on-chain voting. Supported by robotic 3D-printing partners, the framework targets XR adoption: tokenised parametric graphs power virtual configurators for (1:1) design alternatives; users and curators vary parameters within constraints and record reuse on-chain, supporting an inclusive creator economy across the generative process.
Blockchain has been increasingly adopted in various sectors to support transparent and tamper-proof data management. In the academic world, however, reputation systems remain centralized and often fail to represent the true contributions of researchers. Previous innovations such as Dynamic Smart Contracts (DSC) have enabled more flexible interaction models on blockchain, allowing logic and reward schemes to be updated without redeploying contracts. Building on this foundation, this paper introduces a blockchain-based reputation and incentive system tailored for the Decentralized Science (DeSci) ecosystem. The system records academic contributions such as publications, peer reviews, and experimental data into smart contracts that dynamically compute and update reputation scores. Each interaction is validated and permanently stored on-chain, enabling traceable, contribution-based recognition independent of centralized academic institutions. A series of tests conducted on the Ethereum testnet demonstrate that the system operates reliably, supports dynamic rule updates, and effectively tracks contribution-based reputation. This approach enhances transparency and fairness in scientific evaluation, strengthens community-driven validation, and supports the broader vision of DeSci by aligning incentives with openness, accountability, and verifiability.
The ever-growing dependency on blockchain technology to secure e-evidence in a forensic investigation will involve building architecture that is intrinsically tamper-proof and at the same time optimized contextually for investigative purposes. Most of the extant blockchain forensic frameworks incorporate static evaluation models and a monolithic approach to consensus, rendering them ill-suited to the dynamic forensic context of various event sensitivity, legality requirements, and auditability demands. Existing barriers to practical deployment in high-stakes forensic environments have been created. To begin addressing these gaps, this work presents Forensic-Driven Blockchain Evaluation and Simulation Architecture (ForBESA), which provides a complete simulation-based evaluation framework to com- pare Byzantine Fault Tolerant (BFT), Directed Acyclic Graph (DAG), and Proof-of- Stake (PoS) blockchains against forensic key performance indicators (KPIs). This framework consists of five novel modules. First, the Context-Aware KPI Weighting Engine (CAKWE) develops dynamic generation of KPI weight vectors using forensic incident metadata through the use of a decision tree classifier. Second, the Temporal Provenance DAG Tracker (TPDT) constructs enriched DAGs embedding trans- action timelines and investigator metadata to enhance traces’ future availability. Third, evidence routing across Hyperledger, IOTA, and Ethereum 2.0 will be simulated within the Hybrid Ledger Simulation Module under individual KPIs weighted with forensic considerations. Fourth, the Performance-Forensic Tradeoff Analyzer (PFTA) employs a utilitybased optimization and Pareto analysis to identify architecture suitability based on forensic depth versus performance trade-offs. Finally, the Chain-of-Custody Cryptographic Verifier (C3V) ensures evidence integrity and legal admissibility using smart contracts and zero- knowledge proofs. The improved forensic effectiveness and trace reconstruction up to 98.1% accuracy and 100% tamper detection are scantly recorded in the experiments. This study creates the first model of its kind regarding forensic-aware blockchain evaluation. The system provides precise, legally compliant, and context-responsive digital investigations in process.
The sustainable and efficient management of the built environment is a crucial challenge in the increasingly digitalized AEC sector. Innovative technologies such as Building Information Modeling (BIM) and Digital Twin (DT) offer significant opportunities to enhance the operational efficiency and sustainability of physical assets. However, digitalization generates vast amounts of Big Data, and their handling through centralized architectures leads to risks of fragmentation, lack of transparency, and vulnerability to manipulation. In response to these challenges, this study presents an innovative Proof of Concept (PoC) that integrates Blockchain (BT), Digital Twin (DT), and Non-Fungible Token (NFT) technologies to promote decentralized and sustainable data management in the construction industry. The application, called dDT (decentralized Digital Twin), was initially deployed on the Solana blockchain and later integrated with Polygon to leverage EVM compatibility and the ERC-721 standard for NFTs. The platform enables the tokenization of data flows generated by physical assets, ensuring traceability, security, and transparency throughout the entire asset lifecycle. The dDT system represents a sustainable innovation as it creates a secondary data market, fostering collaboration among industry stakeholders and financing new developments through the sale of data-linked NFTs. This decentralized solution addresses fragmentation and transparency issues, promoting more secure, resilient, and sustainable data management practices. The PoC demonstrates how the integration of BT, DT, and NFT can accelerate the transition toward more efficient and innovative practices, with positive impacts on sustainability and technological advancement in the AEC sector.
Ruben Fernando Cuadros Mieses, Adolfo Jorge Prado Ventocilla, Edwin Jorge Montes Eskenazy
Traditional university research funding is frequently undermined by high transaction costs, bureaucratic friction, and information asymmetries that erode donor trust. Blockchain technology, particularly through smart contracts, offers a new paradigm for transparent and efficient fund management. This study investigates the practical viability of this paradigm by developing a decentralized funding platform and conducting a rigorous comparative performance analysis of two leading blockchain infrastructures: Ethereum's Sepolia testnet (a Layer 1 analogue) and Polygon's Amoy testnet (a Layer 2 solution). In 50 consecutive executions per network, the smart contract maintained 100% success on both, yet with Polygon maintaining an average confirmation time of 2.1 seconds versus Sepolia's 5.3 seconds. Furthermore, a preliminary usability study (N=20) confirms that users perceive the Polygon-based platform as significantly higher in performance and overall satisfaction. These findings provide robust quantitative and visual evidence that Layer 2 scaling solutions are not only viable but essential for creating decentralized applications that meet the practical requirements of speed, cost-efficiency, and positive user experience in domains like academic funding. This work contributes a critical empirical benchmark for the emerging field of Decentralized Science (DeSci) and offers a validated architectural model for transparent, automated, and globally accessible research financing.
The demand for organ transplants is growing rapidly, yet the existing systems for organ donation face significant challenges, including lack of transparency, delays, and fraudulent activities. This paper explores a novel approach to address these issues by leveraging blockchain technology. Blockchain offers a decentralized, secure, and tamper-proof environment that can improve the efficiency and reliability of the organ donation process. By incorporating smart contracts and distributed ledger principles, the proposed system ensures that donor and recipient data are securely recorded, access is appropriately regulated, and organ matching and allocation are carried out transparently. The integration of blockchain also enhances trust and minimizes administrative overhead, making the donation process more accountable and streamlined. The study also outlines a conceptual framework for implementing this technology and highlights the potential impact on reducing illegal organ trade and ensuring ethical compliance. The study also explores how blockchain could help in maintaining a nationwide or even global donor registry that is both interoperable and scalable. In doing so, it opens avenues for real-time updates, faster allocation decisions, and the potential to curb illegal organ trafficking. Through a conceptual prototype and system design, the paper illustrates the feasibility of this approach and sets the foundation for future research and real-world implementation.
<ns3:p>The emergence of Web 3.0 and the Metaverse marks a transformative shift in the evolution of the internet and digital ecosystems. This paper explores the foundational principles of decentralization, user autonomy, and data transparency that underpin Web 3.0 technologies, including blockchain, smart contracts, and digital wallets. We analyze how these innovations are reshaping business models, enabling new forms of value creation, and redefining digital ownership and governance. In parallel, we examine the Metaverse as a virtual, immersive environment integrating Web 3.0 infrastructure, and its potential to revolutionize sectors such as logistics, education, finance, and data management. The study also highlights the critical role of a holistic framework encompassing technological, economic, and legal pillars. A special focus is given to data provenance, privacy-preserving computation, and the need for coherent regulatory strategies in light of GDPR, the AI Act, and the Data Act (European Parliament, 2016; European Parliament, 2023; European Parliament, 2024). Finally, we identify emerging challenges related to NFT authenticity, system sustainability, and user experience, proposing a multidisciplinary and lean governance approach to guide future developments.</ns3:p>
We present PromptChain, a decentralized Web3 architecture that establishes AI prompts as first-class digital assets with verifiable ownership, version control, and monetization capabilities. Current centralized platforms lack mechanisms for proper attribution, quality assurance, or fair compensation for prompt creators. PromptChain addresses these limitations through a novel integration of IPFS for immutable storage, smart contracts for governance, and token incentives for community curation. Our design includes: (1) a comprehensive metadata schema for cross-model compatibility, (2) a stake-weighted validation mechanism to align incentives, and (3) a token economy that rewards contributors proportionally to their impact. The proposed architecture demonstrates how decentralized systems could potentially match centralized alternatives in efficiency while providing superior ownership guarantees and censorship resistance through blockchain-anchored provenance tracking. By decoupling prompts from specific AI models or outputs, this work establishes the foundation for an open ecosystem of human-AI collaboration in the Web3 era, representing the first systematic treatment of prompts as standalone digital assets with dedicated decentralized infrastructure.
Alper Alimoğlu, Kamil Erdayandı, Mustafa Mustafa, Ümit Cali
This paper proposes a new decentralized framework, named EDGChain-E (Encrypted-Data-Git Chain for Energy), designed to manage version-controlled, encrypted energy data using blockchain and the InterPlanetary File System. The framework incorporates a Decentralized Autonomous Organization (DAO) to orchestrate collaborative data governance across the lifecycle of energy research and operations, such as smart grid monitoring, demand forecasting, and peer-to-peer energy trading. In EDGChain-E, initial commits capture the full encrypted datasets-such as smart meter readings or grid telemetry-while subsequent updates are tracked as encrypted Git patches, ensuring integrity, traceability, and privacy. This versioning mechanism supports secure collaboration across multiple stakeholders (e.g., utilities, researchers, regulators) without compromising sensitive or regulated information. We highlight the framework's capability to maintain FAIR-compliant (Findable, Accessible, Interoperable, Reusable) provenance of encrypted data. By embedding hash-based content identifiers in Merkle trees, the system enables transparent, auditable, and immutable tracking of data changes, thereby supporting reproducibility and trust in decentralized energy applications.
Marielle S. Gross, Ananya Dewan, Mario Macis, Eve Budd · 9 authors
Introduction Organoids are living, patient-derived tumor models that are revolutionizing precision medicine and drug development, however current privacy practices strip identifiers, thereby undermining ethics, efficiency, and effectiveness for patients and research enterprises alike. Decentralized biobanking “de-bi” applies non-fungible tokens (NFTs) to empower privacy-preserving specimen tracking and data sharing for networks of scientists, donors, and physicians. We design, develop, and demonstrate a functional de-bi platform for a real-world organoid biobank. Methods Ethnography of the organoid biobanking ecosystem was performed in 2022–2023, with site visits, interviews, focus groups, and structured observations of stakeholder interactions. An initial ERC-721 prototype was developed and tested, informing the design of a comprehensive NFT model. Web and mobile app prototypes were developed with a suite of ERC-1155 protocols representing ecosystem constituents as NFTs. We demonstrated the platform with publicly available Human Cancer Models Initiatives organoids to establish proof-of-concept for decentralized biobanking as the foundation of a democratized biomedical metaverse, or “biomediverse.” Results Scientists revealed key challenges for organoid research and development under policy, scientific, and economic constraints of the life science landscape. We advanced decentralized biobanking as a blockchain overlay network solution with potential to overcome barriers, enhance utility and unlock value by uniting collaborators in a privacy-preserving biomediverse. Dedicated smart contracts created “soulbound” NFTs as de-identified digital twins of patients, physicians, and scientists in a networked organoid ecosystem. We modeled biospecimen collection, processing, and distribution, including generation and expansion of organoids, via an auditable on-chain mechanism. Key features included the ability to bootstrap the digital twin NFT model onto an established organoid biobank, visibility of patient-linked biospecimens and related research activities for all ecosystem participants, as well as tooling for multisided data exchange. Implementing de-bi with ERC-1155 showed potential to minimize gas costs of on-chain activity vs ERC-721, though complementary layer-2 solutions will be essential for economic viability. Conclusion Decentralized biobanking has the potential to enhance efficiency, increase translational impact and drive research discovery through implementation of NFT digital twins for organoid research networks. Importantly, this approach also bolsters ethical practices by fostering inclusion, ensuring transparency, and enhancing accountability across the research ecosystem. Next steps include live pilot testing, market design research to align stakeholder incentives, and technical solutions to support a sustainable, scalable and mutually rewarding biomediverse.
Isolated data islands are prevalent in intelligent automated optical inspection (AOI) systems, limiting the full utilization of data resources and impeding the potential of AOI systems. Establishing a collaborative ecology involving software providers, hardware manufacturers, and factories offers an encouraging solution to build a closed-loop data flow and achieve optimal data resource utilization. However, concerns about privacy issues, rights infringement, and threats from other participants present challenges in establishing an efficient and effective community. In this paper, we propose a novel framework, AOI-OPEN, which first creates a trustworthy AOI ecology to gather related entities with decentralized autonomous organization (DAO) mechanisms. Then, a parallel data pipeline is proposed to generate large-scale virtual samples from small-scale real data for AOI systems. Finally, federated learning (FL) is adopted to use the distributed data resources among multiple entities and build privacy-preserving big models. Experiments on defect classification tasks show that, with privacy preserved, AOI-OPEN greatly strengthens the utilization of distributed data resources and improves the accuracy of inspection models.