AI development requires reliable datasets, yet todayâs data supply chains face challenges in traceability, authenticity and ethical compliance. This study introduces a blockchain-assisted data integrity framework that ensures transparent and verifiable provenance for AI model training. The proposed system uses smart contracts to record data lineage, ownership, preprocessing transformations and annotation events. IPFS-based off-chain storage reduces blockchain load while ensuring immutability. A verification engine allows auditors to evaluate dataset compliance with ethical and regulatory standards, including bias mitigation and consent validation. Experiments utilized three real AI workflows: medical imaging, sentiment analysis and environmental sensor classification. Findings show a 92 percent reduction in provenance disputes and an improvement in audit efficiency by 41 percent. The system also provides tamper-resistant documentation supporting responsible AI governance. Latency tests show minimal performance impact due to parallelized validation nodes. This research demonstrates that blockchain can provide a robust backbone for ethical AI ecosystems, where transparency and trust are critical. Future work will explore confidentiality enhancements using zero-knowledge proofs.
Abstract - The paper referenced proposes a decentralized marketplace model for trading, verifying, and managing the ownership of AI models by means of blockchain and NFTs. Given the need for trusted exchange and provenance in the management of AI assets, the authors propose a system wherein AI models and datasets are represented as NFTs on a public blockchain, providing transparent, traceable, and secure transactions. Smart contracts automate auctions, royalty distributions, and ownership transfers. Further security and privacy are provided by TEEs, proxy re-encryption, and decentralized storage (IPFS). Collaboration is enabled through the architecture, which allows contributors to improve and resell models, while royalty schemes guarantee fair compensation for creators. Details of the implementation include smart contracts in Solidity and cost analyses for transaction efficiency. Evaluation in terms of security is resilient against Sybil and Eclipse threats. It is also set up as broadly adaptable to both public and private AI assets and easily generalizable to other situations of digital assets to ensure robust provenance, fair remuneration, and trustless exchange. Key Words: Blockchain, Non-Fungible Tokens(NFTs), Decentralized AI marketplace, Smart Contracts, Digital Ownership
Keybyte Systems, Intentix Lab , Melbourne, Australiapronab@keybytesystems.com.au Supported by AusIndustry Grant IR2405165 __________________________________AbstractModern cloud-native applications distribute business logic across multiple layers: application code, orchestration frameworks, service meshes, and infrastructure configurations. This distribution creates âhidden logicââexecution rules embedded in infrastructure that are invisible during design and difficult to trace at runtime. We present Intention Space , a computing model built on the CPUX (Common Path of Understanding and Execution) paradigm that consolidates all business logic into explicit, design-time declarations using plain-language state pulses. In our model, Design Nodes (DNs) contain computation while Gatekeepers declare execution conditions as named pulses (e.g., âpayment validatedâ: Y). The infrastructure provides only mechanical enforcement through an Intention Loop that matches runtime state to Gatekeepers without adding decision logic. We demonstrate that complex workflowsâtraditionally requiring nested if-then branching and explicit loopsâcan be expressed as linear CPUX sequences where execution paths emerge from data state rather than code branching. Our Golang implementation shows complete elimination of orchestration code while maintaining full cognitive traceability. Beyond technical innovation, CPUX addresses a critical social computing crisis: the lack of accountability in distributed social platforms. By creating unique, device-level CPUX footprints for every interaction, our model enables verifiable traceability from device identity through user intention to executed actionârestoring accountability to social computing while preserving privacy. We argue this separation of intent (CPUX) from enforcement (infrastructure) is essential for building LLM-integrated, auditable, and socially responsible distributed systems.Keywords: CPUX, Intention Space, Design Nodes, Cognitive Computing, Data-Driven Execution, Microservices Architecture, Cloud Computing, LLM Integration, Social Computing Accountability__________________________________1. Introduction1.1 The Hidden Logic ProblemConsider a typical e-commerce order processing system deployed on Kubernetes with Istio service mesh:// order-service/main.go (Business Logic Layer) func ProcessOrder(order Order) error { if order.Amount > 1000 { if err := premiumValidator.Validate(order); err != nil { return retry(premiumValidator.Validate, 3, order) } } else { standardValidator.Validate(order) } // ⊠more branching logic }# k8s/hpa.yaml (Infrastructure Layer) spec: metrics: - type: Resource resource: name: cpu target: type: Utilization averageUtilization: 80 # Hidden rule: Scale when CPU > 80%# istio/retry-policy.yaml (Service Mesh Layer) spec: http: - retries: attempts: 3 perTryTimeout: 2s # Hidden rule: Retry 3 times on failureQuestion: What is the complete execution flow for a $1500 order that fails validation on first attempt?Answer: One must read and correlate:Application code (branching logic)Kubernetes manifests (scaling rules)Istio configurations (retry policies)Service mesh observability logs (runtime behavior)This hidden logic distribution creates fundamental problems:Traceability : No single artifact shows complete flowTestability : Must test infrastructure + code interactionsAuditability : Business stakeholders cannot validate logicMaintainability : Changes require coordinating multiple layersLLM Integration : No structured representation for AI reasoningSocial Accountability : Cannot trace interactions to source devices/users1.2 The Core InsightWe observe that traditional computing conflates two distinct concerns:What should happen(business intent)How to make it happen(mechanical execution)Current architectures intertwine these concerns across code, configuration, and infrastructure, making systems cognitively opaque.Our Contribution: We introduce CPUX (Common Path of Understanding and Execution) , a paradigm that separates business intent from infrastructure enforcement:CPUX Structure : Declares all possible execution paths as sequences of Design Nodes (DNs) with plain-language Gatekeeper conditionsInfrastructure : Provides mechanical execution (Intention Loop) that enforces CPUX declarations without adding decision logicDevice-Level Identity : Each CPUX execution tied to unique device fingerprint + user intention, enabling social computing accountabilityResult : Complete business logic is visible in CPUX; infrastructure remains purely mechanical; every social interaction is traceable1.3 Key ContributionsFormal Model : CPUX as cognitive execution contract with Design Nodes, Intentions, Objects, and Pulses as primitive componentsElimination of Hidden Logic : All business decisions visible in design-time CPUX declarations; infrastructure adds zero decision logicPlain-Language State Declarations : Execution conditions expressed as named pulses (e.g., âinventory confirmedâ: Y) enabling business stakeholder review and LLM integrationData-Driven Execution : Runtime branching eliminated from code; execution paths emerge from pulse state matching via SyncTestSocial Computing Accountability : Device-level CPUX fingerprints create unique, traceable identity for every social interaction, addressing the accountability crisis in platforms like Facebook, Twitter, TikTokImplementation & Evaluation : Golang framework code sample with concrete use case demonstrating zero orchestration code while maintaining full traceability1.4 Paper OrganizationSection 2 examines related work. Section 3 presents the PnR computing model and CPUX formalism. Section 4 details the architecture and implementation. Section 5 evaluates our approach through metrics and case studies. Section 6 discusses LLM integration. Section 7 introduces CPUX for social computing accountabilityâthe urgent global need. Section 8 concludes with future directions.__________________________________2. Related Work2.1 Workflow Orchestration SystemsAWS Step Functions [1] and Azure Logic Apps [2] provide visual workflow definition with explicit state machines. However, they:Use proprietary JSON/XML DSLs (not plain language)Embed conditional logic in workflow definitions (still branching)Remain platform-specific (vendor lock-in)Require reading workflow definitions to understand flowCannot trace to device/user identityApache Airflow [3] and Temporal [4] define workflows as code with DAG structures. They improve on step functions ,recoverability but:Business logic still in code (if-then branches)Workflow orchestration separate from executionNo plain-language condition declarationsNo device-level traceabilityCPUX Advantage : All logic in plain-language pulses, platform-agnostic, no explicit branching in declarations, device-level identity for every execution, recoverability built into platform. 2.2 Service Mesh & OrchestrationIstio [5] and Linkerd [6] provide traffic management, retries, circuit breaking. Kubernetes Operators [7] encode reconciliation logic. These systems:Hide business rules in YAML configurationsDistribute logic across mesh config + operator codeFocus on infrastructure concerns (not business flow)Lack unified view of complete execution pathNo user/device attributionCPUX Advantage : Consolidates all execution logic in CPUX; infrastructure config aligned with business intent; device identity integral.2.3 Event-Driven ArchitecturesApache Kafka [8], AWS EventBridge [9] enable event-driven systems with loose coupling. Reactive systems [10] promote message-passing. However:Event flows implicit (must trace message paths)Conditional logic in event handlers (code-level branching)No design-time declaration of all possible flowsNo provenance tracking to source deviceCPUX Advantage : Explicit declaration of all event-driven paths as DN sequences with visible Gatekeepers; device identity in event provenance.2.4 Intent-Based SystemsIntent-Based Networking [11] translates high-level intents to network configurations. Policy-based management [12] separates policy from mechanism. Closest to our work, but:Focus on infrastructure (not application logic)Policies often domain-specific (not general computing)Limited plain-language expressivenessNo user accountabilityCPUX Advantage : General-purpose computing model with full plain-language pulse declarations applicable to any domain; device-level user accountability.2.5 Formal Methods & Model CheckingTLA+ [13], Alloy [14], and Petri Nets [15] enable formal specification and verification. These are powerful but:Require specialized formal notation (high learning curve)Specification separate from implementation (sync problems)Not designed for runtime executionNo social computing traceabilityCPUX Advantage : Declarations are executable; CPUX structure IS the implementation contract; device identity embedded.2.6 Social Computing & AccountabilityBlockchain-based identity [16] and zero-knowledge proofs [17] address digital identity but:Focus on cryptographic primitives (not execution tracing)Donât integrate with application logicNo cognitive representation of intentFederated social networks [18] (Mastodon, ActivityPub) improve decentralization but:Still lack device-level traceabilityNo structured intent representationCannot prove user intended specific actionCPUX Advantage : First system to integrate device identity, user intention, and execution trace in single cognitive framework.2.7 PositioningCPUX is the first system to combine:Plain-language execution conditions (like Intent-Based Networking)Executable specifications (unlike formal methods)Complete flow visibility (unlike distributed orchestration)Zero hidden infrastructure logic (unique contribution)Device-level social accountability (unique contribution)__________________________________3. The PnR Computing Model3.1 Core Abstractions3.1.1 Pulse: Atomic State UnitA Pulse is the fundamental data unit representing a named state with optional response and trivalence:Pulse = (Name: String, Response: Value, Trivalence: {Y, N, U})Name : Plain-language identifier (e.g., âpayment validatedâ)Response : Optional value (e.g., transaction ID)Trivalence : Y (yes/true), N (no/false), U (undecided)
AI systems rely heavily on high-quality training data, yet provenance tracking remains fragmented and vulnerable to manipulation. This study presents a blockchain-enabled data provenance framework designed to bring transparency and verifiability to AI training pipelines. The architecture records dataset lineage, preprocessing steps, annotation events and model updates using immutable smart contract transactions. A lightweight off-chain storage mechanism reduces blockchain overhead while maintaining audit guarantees. The system was tested with three machine learning pipelines involving image classification, text processing and sensor analytics. Results show a 95 percent reduction in provenance disputes and full traceability across all data contributors. Smart contracts automate compliance checks and access permissions, ensuring that only validated datasets feed into the training process. The framework improves accountability for AI ethics, model bias evaluation and regulatory reporting. Experiments confirm that blockchain latency does not significantly affect pipeline throughput due to parallelized validation nodes. The work demonstrates how decentralized technologies can support trustworthy AI development. Future research will explore integration with zero-knowledge proofs to further enhance confidentiality.
ExecMesh introduces cryptographically verifiable computation as a foundational primitive for regulatory compliance and audit trail requirements in AI/ML systems [1â3]. By combining commitmentbased verification with secure multi-party oracles and a two-tier regulatory architecture, ExecMesh enables enterprises to meet FDA, SEC, and EU AI Act requirements while maintaining the benefits of decentralized infrastructure. Immediate Value Proposition: ExecMesh provides immediate value as an audit trail and provenance layer for regulated AI systems, independent of advances in zero-knowledge proof technology. Even without full verification of large neural networks, the system delivers cryptographic guarantees for data integrity, execution timestamps, and pipeline reproducibilityâmeeting core regulatory requirements today.
Rene Casanova, FernĂĄn A Villa-GarzĂłn, John W. Branch
Background: Health information systems (HIS) are critical for digital health transformation, yet fragmentation and poor interoperability adoption remains a major challenge. Objectives: This study systematically reviews architectural patterns used in HIS and evaluates their alignment with ecosystem-level requirements. Methods: Following PRISMA 2020 guidelines, a systematic literature review was conducted across Scopus, IEEE Xplore, PubMed, and Web of Science (2020-2025). Eligible studies described, evaluated, or proposed HIS solutions. Results: From an initial set of 304 records, 89 met the inclusion criteria. Service-based and decentralized/distributed ledger architectures were predominant, with emerging models integrating edge computing and modular design. FHIR-based contracts are found as stabilizers of interfaces, enabling validation and reducing integration costs. However, gaps persist in cross-border care, sustainability, and artificial intelligence integration. Conclusion: While microservices dominate current HIS architectures, achieving resilient, interoperable ecosystems requires greater architectural diversity and intersectoral collaboration.
Recent advances in large language models (LLMs) have enabled the emergence of intelligent agents capable of performing complex multi-step tasks across various domains. In parallel, the growth of Web3 has introduced a decentralized web infrastructure, yet remains largely inaccessible to non-technical users due to operational complexity, fragmented information, and security risks. In this article, we present Web3Agent , an AI agent system that integrates LLM-based interaction with blockchain environments to enable language-driven on-chain operations. Web3Agent automatically decomposes user instructions into structured workflows, dynamically queries blockchain data and APIs, and performs multi-step operations such as asset transfers, token swaps, and smart contract execution. Web3Agent incorporates real-time inspection, error handling, and interaction transparency across its operation log, and flow visualization components. We evaluate the system and perform ablation study with customized dataset in a simulated environment, demonstrating its feasibility in orchestrating complex Web3 tasks and highlighting implications for agent-based abstraction in decentralized systems.
J. Wenzel, Alam, Syeda Umaima, Andreas Schmidt, Hanwei Zhang · 5 authors
An ever increasing number of high-stake decisions are made or assisted by automated systems employing brittle artificial intelligence technology. There is a substantial risk that some of these decision induce harm to people, by infringing their well-being or their fundamental human rights. The state-of-the-art in AI systems makes little effort with respect to appropriate documentation of the decision process. This obstructs the ability to trace what went into a decision, which in turn is a prerequisite to any attempt of reconstructing a responsibility chain. Specifically, such traceability is linked to a documentation that will stand up in court when determining the cause of some AI-based decision that inadvertently or intentionally violates the law. This paper takes a radical, yet practical, approach to this problem, by enforcing the documentation of each and every component that goes into the training or inference of an automated decision. As such, it presents the first running workflow supporting the generation of tamper-proof, verifiable and exhaustive traces of AI decisions. In doing so, we expand the DBOM concept into an effective running workflow leveraging confidential computing technology. We demonstrate the inner workings of the workflow in the development of an app to tell poisonous and edible mushrooms apart, meant as a playful example of high-stake decision support.
The ethical tension surrounding AI-generated art often arises from misconceptions that anthropomorphize the algorithmic process. The accusation that âAI steals human creativityâ overlooks the mediating role of human design and data literacy. This paper reframes the debate as a problem of informational asymmetry rather than morality. It proposes that Non-Fungible Tokens (NFTs) and Digital Object Identifiers (DOIs) can visualize and authenticate the flow of creative tension within a transparent ecosystem. NFTs serve as formal anchorsârecording authorship, signature, and temporal originâwhile DOIs preserve the conceptual framework and creative process. When linked, these two systems transform authorship into a traceable circulation of knowledge, allowing the boundary between plagiarism, homage, and originality to be objectively determined. This dual-layer provenance model presents an ethical infrastructure for creation in the age of generative AI.
Open access
2 source records
Scientific Computing and Data Management
Ethics and Social Impacts of AI
Artificial Intelligence in Healthcare and Education
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.
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 modelâs ability to surface context-specific âbest fitsâ 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 DeepSeekâs 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.
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.
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 (2018â2025), 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.
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.
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 AlimoÄlu, Kamil Erdayandı, Mustafa Mustafa, Ămit 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 âde-biâ 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 2022â2023, 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 âbiomediverse.â 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 âsoulboundâ 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.