Background Cross-border credit recognition in Sino-Foreign Cooperative Education (SFCE) suffers from data fragmentation, regulatory conflicts (e.g., GDPR vs. China’s Data Security Law), and low efficiency. Objective This paper proposes ZkHybridChain, a dual-layer blockchain credit bank (BCB) framework to resolve the privacy-compliance-efficiency trilemma. Methods The hybrid architecture integrates Polygon zkEVM (public credential hashing) and Hyperledger Fabric (private raw data storage). Zero-Knowledge Proofs (ZKP) and three-tiered smart contracts enable automated credit conversion (ECTS↔CNQF) and privacy-preserving verification. Results Experiments on 10,000 SFCE records show 58% efficiency gain (full lifecycle from ∼1,200 s to <9 s), cross-border latency <9 s, throughput up to 1,620 TPS, and ZKP verification latency 135 ms (93.7% success rate). Conclusion ZkHybridChain provides a scalable, GDPR/DSL-compliant solution for global education trust networks. Future work includes post-quantum cryptography and lightweight client protocols.
AgisFL v5.0 Autonomous Federated Learning Ecosystem Citation: Yadav, A. (2026). AgisFL v5.0: Autonomous Federated Learning Ecosystem with Privacy-Preserving Explainability and Enterprise AI Orchestration. Zenodo. https://doi.org/10.5281/zenodo.20363208 Table of Contents Executive Summary Abstract Introduction Industry Challenges in Federated Learning Research Objectives Literature Review System Overview Core Architectural Design Autonomous AI Engine Federated Learning Core Three-Line Integration Framework Security Architecture Privacy Preservation Framework Federated Explainability System Concept Drift Detection and Adaptive Retraining Distributed Systems Design Enterprise Governance Layer Monitoring and Observability API and Communication Architecture Database and Storage Infrastructure CI/CD and Release Engineering Kubernetes and Cloud Deployment Architecture Threat Modeling and Adversarial Defense Real-World Industry Applications Benchmarking and Performance Evaluation Comparative Analysis Scalability and Reliability Engineering Testing and Validation Framework Compliance and Regulatory Readiness Research Contributions Limitations Future Research Directions Conclusion References Appendices 1. Executive Summary AgisFL v5.0 is a next-generation autonomous federated learning ecosystem engineered to redefine how distributed artificial intelligence systems are developed, deployed, optimized, governed, and scaled in enterprise environments. The platform introduces a unified architecture that combines: Autonomous AI orchestration Federated machine learning Privacy-preserving analytics Enterprise governance Federated explainability Real-time monitoring Distributed optimization Security-first infrastructure Zero-trust operational principles Developer-centric integration abstractions Modern federated learning systems frequently suffer from fragmented tooling, operational complexity, difficult deployment procedures, weak observability, limited explainability, and insufficient enterprise governance. AgisFL addresses these limitations through a fully integrated ecosystem capable of autonomous optimization, adaptive retraining, drift monitoring, federated explainability, and production-grade orchestration. A major innovation introduced in AgisFL v5.0 is the Three-Line Integration SDK, which reduces federated learning implementation complexity from hundreds of lines of orchestration code into a simplified developer abstraction requiring only three operational commands. AgisFL also introduces: FedNAS (Federated Neural Architecture Search) FedHPO (Federated Hyperparameter Optimization) AutoFL autonomous orchestration engine Federated SHAP explainability framework Real-time drift detection systems Enterprise governance tooling Distributed observability infrastructure Autonomous retraining pipelines Integrated red-team simulation systems The platform is designed to support enterprise-grade deployments across: Healthcare AI Banking and fraud detection Cybersecurity analytics Autonomous transportation systems Industrial IoT ecosystems Smart infrastructure Defense intelligence systems Cross-organizational research networks AgisFL transforms federated learning from a research-heavy distributed systems problem into an operational autonomous AI platform suitable for enterprise production environments. 2. Abstract Federated learning has emerged as one of the most important paradigms in modern artificial intelligence because it enables collaborative machine learning without centralized raw data collection. Despite significant advances in federated optimization algorithms, practical enterprise adoption remains constrained by engineering complexity, infrastructure fragmentation, weak observability, insufficient explainability, operational overhead, and inadequate governance tooling. This paper introduces AgisFL v5.0, an enterprise-grade autonomous federated learning ecosystem designed to simplify distributed AI development while preserving privacy, scalability, explainability, and enterprise operational resilience. The proposed architecture integrates autonomous orchestration, federated neural architecture search, hyperparameter optimization, differential privacy, federated explainability, real-time telemetry, adaptive retraining, distributed governance, and multi-tenant deployment capabilities into a unified operational platform. A key contribution of this work is the introduction of a Three-Line Integration abstraction layer that reduces federated learning implementation complexity by approximately 98%, enabling developers to operationalize distributed machine learning workflows with minimal infrastructure overhead. Experimental evaluation demonstrates: Significant reduction in deployment complexity Faster convergence behavior Enhanced privacy guarantees Improved operational resilience Lower infrastructure overhead Enhanced governance visibility Autonomous optimization capabilities Enterprise-grade scalability The findings suggest that federated learning ecosystems can evolve beyond isolated research frameworks into fully autonomous enterprise-operational AI infrastructures capable of supporting large-scale real-world deployments. 3. Introduction Artificial intelligence systems increasingly depend on access to large-scale distributed datasets. However, centralized data aggregation introduces major concerns related to: Privacy Regulatory compliance Infrastructure cost Data ownership Security risk Cross-border governance Operational complexity Federated learning addresses these concerns by enabling decentralized collaborative model training where data remains localized while model updates are aggregated centrally or hierarchically. Despite its promise, enterprise adoption of federated learning remains limited due to several fundamental issues: 3.1 Complexity of Distributed Orchestration Traditional federated learning infrastructures require: Client synchronization systems Custom networking layers Aggregation orchestration Distributed storage pipelines Manual security implementation Complex deployment workflows These systems introduce substantial engineering overhead. 3.2 Limited Explainability Most federated learning frameworks prioritize optimization performance while neglecting explainability and interpretability requirements. This creates significant barriers in regulated domains such as: Healthcare Finance Cybersecurity Defense 3.3 Weak Enterprise Governance Existing systems frequently lack: Auditability Compliance tooling Enterprise observability Governance automation Operational telemetry Real-time incident response 3.4 Operational Fragility Distributed environments are inherently dynamic. Existing federated systems rarely support: Autonomous retraining Drift adaptation Self-healing infrastructure Dynamic client balancing Adaptive optimization AgisFL v5.0 was designed specifically to address these challenges. 4. Industry Challenges in Federated Learning 4.1 Data Sovereignty Constraints Modern organizations operate under increasingly strict regulatory environments including: GDPR HIPAA PCI-DSS ISO 27001 SOC2 NIST frameworks Centralized AI architectures frequently violate data locality requirements. 4.2 Security Risks Federated systems are vulnerable to: Model poisoning Data poisoning Gradient inversion attacks Membership inference attacks Byzantine participants Adversarial manipulation 4.3 Infrastructure Fragmentation Organizations often rely on heterogeneous environments: Cloud providers On-premise systems Edge devices Hybrid deployments Multi-region clusters This creates interoperability challenges. 4.4 Operational Scalability Large federated ecosystems require: Distributed orchestration Fault tolerance Client balancing Scheduling systems Autonomous optimization Resource-aware coordination 5. Research Objectives The primary research objectives of AgisFL v5.0 include: Objective 1 — Simplification Reduce federated learning deployment complexity through abstracted developer interfaces. Objective 2 — Autonomous AI Operations Enable self-optimizing distributed AI infrastructure. Objective 3 — Privacy Preservation Maintain strong privacy guarantees without sacrificing operational intelligence. Objective 4 — Explainability Provide interpretable federated learning workflows. Objective 5 — Enterprise Governance Introduce scalable governance and observability tooling. Objective 6 — Production Readiness Support real-world enterprise deployment scenarios. 6. Literature Review Federated learning was initially formalized by Google researchers to enable collaborative learning across decentralized mobile devices. Subsequent frameworks introduced: FedAvg optimization FedProx adaptive training Differential privacy systems Secure aggregation protocols Decentralized optimization methods However, existing systems frequently remain research-oriented. 6.1 Existing Framework Limitations Platform Limitation TensorFlow Federated Research-focused complexity Flower Limited autonomous optimization PySyft Operational deployment complexity OpenFL Limited explainability integration FedML Weak governance tooling AgisFL differentiates itself through autonomous orchestration, explainability integration, enterprise governance, and simplified deployment abstractions. 7. System Overview AgisFL v5.0 is composed of multiple inte
As large language models (LLMs) grow in scale and are predominantly served from remote platforms, verifying faithful inference execution becomes critical (i.e., ensuring that a provider actually executes the advertised model and computational workload rather than a tampered or downsized variant). Zero-knowledge (ZK) LLM inference offers an appealing approach. It promises public verifiability and delivers per-instance guarantees of equational correctness by proving that an output is consistent with executing a public architecture under committed, private weights. Though, we show that it does not bind the effort expended to produce the output. In this paper, we formalize this overlooked effort gap and introduce the Hollow-LLM Attack, in which a dishonest provider retains the declared architecture and parameter count but embeds ghost weights whose algebraic structure collapses effective computation. These witnesses satisfy the verification circuit and yield valid proofs, even though the dishonest model owner, who serves as the prover, performs computation commensurate with a much smaller model than the declared public architecture. This creates a profitable equilibrium in which providers deliver provably correct outputs at small-model cost while overclaiming model size. Accordingly, we characterize concrete families of ghost weights that compose with standard transformer blocks and show that such hollow deployments substantially reduce serving cost with zero quality loss under the same verification circuit. These findings underscore that proof of correct inference is not proof of large-model execution and necessitate additional protections to bind correctness to verifiable computational work.
The tremendous progress of medical foundation models has proven to be groundbreaking in meta-analysis of clinical prediction, diagnosis, and multimodal healthcare analytics, but the development of medical foundation models is limited due to stringent data privacy concerns, cross-institutional trust issues, and security risks in a collaborative learning environment. Traditional federated learning allows for distributed training of the model with no central sharing of data but is prone to poisoning of the model, inference attacks, and low verifiability of participating institutions. This study proposes an idea of Autonomous Trust and Zero-Knowledge Blockchain Framework (AT-ZKBF) for Federated Medical Foundation Models, to establish decentralized trust, cryptographic verifiability and secure collaboration among heterogeneous healthcare providers. The framework combines the foundation model training in a federated peer-to-peer setup, the permissioned blockchain network for trust orchestration and mechanisms using the zero-knowledge proof (ZKP) for model updates to avoid the content of sensitive parameters of the model. Every local update is cryptographically authenticated with zk-SNARK-based zero-knowledge proofs that check proper gradient descent running and limited limit on updates without exposing private gradients or data. A reputation-driven trust scoring module automatically scores the reliability of participants. Experimental evaluation done on a BraTs, a multi-institutional medical imaging dataset shows that the proposed framework can get 96.4% classification accuracy (up 4.8% vs. standard federated learning) with poisoning model control decreased by 63% and communication overhead reduced by 21% by optimized blockchain batching. Security analysis makes sure of the robustness from gradient inferences and Byzantine attacks. The validation upon integration of autonomous trust computation, and zero-knowledge cryptography to blockchain enabled federated learning substantially adds to security, transparency and scalability for collaborative medical foundation model training providing a probable way forward to privacy preserving trust worthy AI in healthcare ecosystems.
The rapid evolution of Decentralized Finance (DeFi) has introduced unprecedented financial innovations alongside complex fraud vectors that challenge conventional security mechanisms.Traditional fraud detection systems rely heavily on centralized data aggregation and opaque machine learning models, which are fundamentally incompatible with the decentralized and trust-minimized architecture of blockchain ecosystems.Emerging paradigms such as Federated Learning (FL) and Explainable Artificial Intelligence (XAI) have been independently proposed to address privacy and transparency concerns in financial systems.However, despite significant progress in each domain, the literature reveals methodological fragmentation and architectural disconnection among blockchain-based fraud detection, privacy-preserving learning, and explainability mechanisms.This study critically reviews existing research on traditional finance fraud detection, blockchain analytics, federated learning security, XAI applications, and blockchain-FL integration frameworks.Through comparative and analytical synthesis, it identifies critical research gaps, including the absence of unified decentralized fraud architectures, insufficient explainability in on-chain systems, and limited governance models for federated financial intelligence.This study establishes a theoretical and technological foundation for an integrated blockchain-driven FL-XAI framework tailored for DeFi fraud detection.
The interplay between the advancements in quantum computing techniques and the adoption of the distributed learning approach pose an enormous challenge to conventional cryptographic authentication protocols. Traditional public key systems and federated learning (FL) authentication methods based on the hardness of solving the integer factorization problem or discrete logarithms become inefficient due to the existence of Shor’s algorithm. This paper gives a detailed review of the latest research efforts toward the development of efficient and secure privacy-preserving FL authentication methods based on Post-Quantum Cryptography (PQC). In particular, we present the state-of-the-art of three schemes, namely, PQBFL (Post-Quantum Blockchain-based Federated Learning), ZKFL-PQ (Zero-Knowledge Federated Learning with Lattice-Based Encryption), and Enhanced EAADE for vehicular networks. It is shown that lattice-based authentication is both computationally efficient (signing times of around 0.65 ms) and robust against quantum attacks. Our proposed hybrid scheme is comprised of ML-KEM for key encapsulation, ML-DSA-65 for digital signatures, and Zero-knowledge proof for gradient integrity verification. The empirical evaluation shows a reduction of 44.96% in the computation cost and 22.16% in the communication cost relative to the class.
Ms. Anushka Prasad Joshi, Prof. Sachin Bhosale, Dr. Shubhangi Gunjal, Dr. Anand Khatri
Abstract: With real estate markets digitalizing at a remarkable pace, there's a growing — and largely unmet — need for machine learning systems that can harness multi-institutional data without putting privacy or regulatory standing at risk. In this paper, we present a Privacy-Preserving Federated Learning (PP-FL) framework built specifically for digital real estate ecosystems. Our approach lets distributed stakeholders — property agencies, government land registries, financial institutions, and PropTech platforms — collaboratively train predictive models without ever pooling their raw transaction or personal records in one place. We've designed the system around three interlocking privacy layers: a DP-SGD-based differential privacy optimizer, a homomorphic encryption scheme for gradient transmission, and a secure multi-party computation protocol to safeguard intermediate model states. On top of that, a blockchain-backed audit mechanism using zero-knowledge proofs provides verifiable, regulator-friendly compliance. When we tested the framework on a simulated dataset of 2.4 million real estate transactions spanning multiple institutional clients, it achieved 91.8% prediction accuracy — just 2.4 percentage points behind a fully centralized model — while holding the differential privacy budget to ε = 0.5, cutting communication overhead by 65% relative to naive federated approaches, and satisfying both GDPR and RERA requirements. We believe these results make a strong case that high-utility, privacy-first collaborative learning is not just theoretically possible but practically deployable in today's real estate sector. Keywords: Federated Learning, Differential Privacy, Homomorphic Encryption, Real Estate Analytics, Secure Multi-Party Computation, Blockchain, GDPR Compliance, Data Sovereignty, PropTech, Zero-Knowledge Proofs.
Federated Learning (FL) enables collaborative model training while preserving data privacy but relies on centralized aggregation servers, leading to issues such as lack of transparency, vulnerability to malicious updates, and single points of failure. This paper proposes a decentralized federated learning framework integrating blockchain technology and the InterPlanetary File System (IPFS) to eliminate central authority and enhance trust. Smart contracts deployed on the Ethereum Sepolia testnet manage model submission, validation, incentive distribution, and reputation tracking. Model updates are stored off-chain using IPFS, while their hashes are recorded on the blockchain to ensure integrity and immutability. A staking and slashing mechanism is introduced to encourage honest participation, where valid contributions are rewarded and malicious updates are penalized. A reputation system further evaluates participant reliability over time. The system is implemented using PyTorch, Solidity, Web3.py, and React.js. Experimental results demonstrate improved security, transparency, and efficient decentralized coordination, highlighting the feasibility of integrating federated learning with blockchain and decentralized storage for scalable and trustworthy machine learning applications.
Swati Sachan, Dale Fickett, Richard Buchinger, Theo Miller
Recent advances in error-corrected qubits have accelerated the timeline for practical quantum computing. It poses a threat to cryptographic primitives used to secure financial systems, government infrastructure, communication networks, and DeFi (Decentralized Finance) ecosystems. This paper introduces a post-quantum secure federated DeFi framework that enables inter-bank collaboration to improve the inclusivity of individuals underserved by local lenders due to limited financial histories. Multiple banks contribute encrypted information batches to a virtual server, where lattice-based Fully Homomorphic Encryption (FHE) enables end-to-end homomorphic computation. The server fuses local data-driven probabilistic assessments, expert beliefs, and verifiable evidence generated by the NASA-IBM Prithvi Geospatial Foundation Model (GFM), in encrypted format. Decentralized technologies are employed to ensure tamper-proof evidence and auditable accountability for all encrypted data exchanges between institutions and the server. The framework is tested on agricultural lending decisions for rural borrowers in Virginia.
Sixth-generation (6G) networks are expected to provide ubiquitous connectivity, AI-native orchestration, and seamless integration across terrestrial and non-terrestrial infrastructures. However, these capabilities introduce new privacy challenges related to the classification and protection of personal, quasi-personal, and non-personal data in complex data-driven environments. This paper presents a systematic review of 78 peer-reviewed studies published between 2019 and 2025. Following a PRISMA-based methodology, this review analyzes privacy-enhancing technologies (PETs), regulatory compliance frameworks, and architectural patterns for privacy preservation in 6G networks. The findings show that differential privacy (DP) and federated learning (FL) dominate current research, accounting for nearly 52% of the reviewed studies. Blockchain auditing and zero-knowledge proofs (ZKPs) collectively represent approximately 30%, while the remaining mechanisms, including physical-layer security (PLS), trusted execution environments (TEEs), homomorphic encryption (HE), secure multi-party computation (SMPC), and anonymization, account for roughly 18%. These mechanisms exhibit varying levels of privacy strength, utility preservation, latency, and energy cost. At the same time, evolving regulatory frameworks, including GDPR, PDPL, CCPA/CPRA, LGPD, and PIPL, increasingly extend privacy obligations to quasi-personal and aggregated data. Building on these findings, this paper proposes a unified taxonomy that clarifies the boundary between personal and non-personal data. It also provides a cross-layer mapping between PETs and compliance requirements across the Core/SBA, RAN, Edge/MEC, and NTN layers. Finally, this paper presents a forward-looking roadmap for 2025–2030, highlighting hybrid PET pipelines, post-quantum auditability, and AI-driven compliance automation as key directions for privacy-preserving 6G standardization.
The European Commission's April 2026 age verification framework, built on software-based Zero-Knowledge Proofs (ZKP) atop the European Digital Identity (EUDI) Wallet, fails to achieve its stated privacy guarantees due to a structural enrollment binding problem: any ZKP scheme whose trust root is a government identity credential inherits that credential's linkability at the point of issuance. This paper proposes a replacement architecture based on hardware bearer credentials — physically issued FIDO2 tokens whose identity binding is discarded immediately after issuance — combined with an anonymous hardware-handle revocation list, offline kiosk enrollment, and a self-funding economic model. The proposal is technically feasible with current production technology, financially viable at EU procurement scale, and operationally self-sustaining through a €10 citizen co-payment at issuance plus a €30 replacement fee. A cost model for national deployment using Italy as a case study demonstrates that the system requires near-zero net public expenditure. The primary novel contribution is a game-theoretic mechanism embedded in the replacement fee structure that renders secondary market trading of credentials economically irrational without requiring any surveillance of credential holders.
Data valuation is a foundational task in data marketplaces, where a Shapley-value attribution determines how a buyer's payment is distributed among data providers. Typically, the marketplace operator runs this attribution alone, requiring participants and external auditors to trust scores they cannot independently recompute on the underlying private data. While zero-knowledge proofs (ZKPs) can theoretically reconcile this conflict between privacy and verifiability, existing ZK valuation systems fail to scale to real-world marketplace demands due to prohibitive proving times or the requirement to disclose validation cohorts. We present ZK-Value, a practical, end-to-end ZK data-valuation system. Our solution bridges the scalability gap through a fully co-designed architecture: (1) LSH-Shapley, a locality-based valuation primitive that replaces expensive pairwise distance metrics with per-bucket collision counts; (2) ZK-LSH-Shapley, a tailored ZKP protocol that drastically reduces witness size by encoding these counts into bucket-level histograms rather than naive per-pair tensors; and (3) structural proof-system optimizations, specifically super-oracle batching and sparsity skipping. Evaluated across 12 standard datasets, ZK-Value delivers valuation quality on par with state-of-the-art baselines (within 0.033 AUROC of exact KNN-Shapley), while generating proofs in seconds to minutes and outperforming specialized ZK baselines by 12.6x to 68.1x in proving time, with verification in under 4.6 s.
The intersection of Artificial Intelligence (AI) and distributed systems has given rise to Federated Learning (FL), a paradigm that enables decentralized model training without compromising local data privacy. As organizational data silos grow, deploying complex machine learning models across highly distributed edge networks becomes a critical infrastructural challenge. Standard FL implementations suffer from severe vulnerabilities related to adversarial gradient updates and computational bottlenecks at the aggregation layer. This paper presents a novel, end-to-end distributed architecture that hardens FL pipelines using advanced cryptographic verification and optimized big data processing frameworks. We introduce a Zero-Knowledge Proof (ZKP) wrapper that cryptographically validates node computations before global aggregation, neutralizing model poisoning attacks without inspecting raw gradients. Additionally, we evaluate the system's performance using extreme gradient boosting models optimized for distributed edge execution. We formalize the mathematical transformation of the machine learning loss functions into Rank-1 Constraint Systems (R1CS) suitable for succinct verification. Extensive experimental results demonstrate that our hybrid architecture achieves a 94.2\% accuracy retention under adversarial conditions while maintaining scalable throughput across 1,000 parallel distributed nodes, effectively bridging the gap between rigorous cryptographic security and high-performance distributed AI.
Electronic Healthcare Records (EHRs) provide distributed access to patient and doctor information through pervasive cloud-based storage. As this data is highly sensitive, robust privacy measures are essential to mitigate adversarial impacts. To ensure optimal privacy across multiple shared EHRs, this article proposes a Session-dependent Zero Knowledge Proof Technique (SZKPT). The framework identifies privacy breaches using two truth values: the first representing optimal session closure, and the second reflecting verification at each sharing instance. Both truth values are validated through iterated session validations, which are managed using a deep learning paradigm. During training, different combinations of truth values are employed to maximize privacy during data sharing, while iterative processes train consecutive validation instances to improve breach detection. Truth values are continuously updated to reflect the session closure and the most recent privacy verification. In practice, if either truth value equals zero, the session is suspended; otherwise, if truth values are valid in consecutive iterations, data sharing is delegated to the authorized user. The process is repeatted at regular intervals with updated truth values, ensuring continuous monitoring and adaptive privacy protection. The proposed technique is rigorously evaluated using key performance metrics, including access verification, computational complexity, privacy breach detection, verification time, and access delegation time. Results demonstrate that SZKPT effectively balances privacy preservation with usability, providing a reliable, scalable, and efficient solution for secure EHR management in cloud-based healthcare systems.
In IIoT situations, federated learning (FL) is a way to use industrial data that protects privacy. At the same time, adding blockchain to federated learning training makes it more trustworthy. But there are still some big problems with current blockchain-based FL frameworks: 1) The current consensus mechanisms don't do a good job of filtering out bad devices, which lets low-quality participants mess with global model training and make the model less robust; 2) Current privacy budget strategies are too simple, making it hard to find a balance between protecting privacy during statistical queries and gradient updates. Strong privacy protection lowers model accuracy, while weak protection doesn't protect against poisoning attacks. This paper proposes ShieldDFL, a blockchain-based federated learning framework with dual privacy protection and reputation-driven consensus, to solve these problems. This method uses a hybrid consensus mechanism based on LSTM-based reputation scoring to dynamically assess both short-term and long-term device contributions. This makes it possible to choose the best devices with accuracy. At the same time, it adds a new dual privacy budget mechanism that uses differential privacy for both statistical queries and gradient updates. This keeps privacy strong while keeping the model's performance high. The proposed method lowers the chances of bad devices getting into the consensus pool to 1.5%, lowers the success rates of SAR and BASR attacks to 5.8% and 2.1%, respectively, and keeps the model's accuracy high at 98.1% on MNIST and 87.6% on CIFAR-10. In general, the proposed framework does a good job of getting around the security and privacy problems that come with blockchain-based federated learning. It offers a fast and flexible way for decentralised and trustworthy collaboration in IIoT situations.
In the digital era, personal data is continuously collected, processed, and shared by organizations across various sectors. Traditional consent management systems suffer from centralization, opacity, and insufficient user control, making it difficult for individuals to track and enforce their data-sharing preferences. This paper presents ConsentChain, a decentralized consent governance protocol built on the Polygon blockchain. The system leverages Ethereum-compatible smart contracts to implement purpose-bound, time-limited, and user-revocable consent records, backed by an immutable on-chain audit trail. The architecture employs two core Solidity smart contracts—ConsentManager and AccessController—supported by a React-based frontend and MetaMask wallet integration. Comprehensive end-to-end testing demonstrates 18 of 18 test cases passing, validating the correctness of consent lifecycle management, access validation, role-based access control, and event logging. ConsentChain demonstrates that blockchain technology can provide a transparent, tamper-proof, and user-sovereign alternative to conventional consent management systems, with clear pathways toward enterprise adoption, multi-chain deployment, and zero-knowledge privacy extensions. Index Terms—Blockchain, Consent Management, Smart Contracts, Data Privacy, GDPR, Decentralized Systems, Ethereum, Polygon, Access Control, Audit Trail.
Healthcare data is among the most sensitive and frequently targeted information in the digital era. Existing centralised Electronic Health Record (EHR) systems are vulnerable to data breaches, unauthorised access, and single-point failures. This paper introduces a blockchain-based privacy-preserving framework for healthcare data management that integrates smart contracts, attribute-based encryption (ABE), and zero-knowledge proofs (ZKP) to ensure tamper-proof, role-gated data access. Our system is deployed on a permissioned Hyperledger Fabric network with a RESTful API gateway and a React-based patient portal. Experimental evaluations show that the proposed system achieves 99.2% access-control policy enforcement, reduces unauthorised access incidents to near zero, and maintains record retrieval latency under 180 ms at the 95th percentile. The framework also supports HIPAA and GDPR compliance through immutable audit trails and consent lifecycle management
<sec> <title>BACKGROUND</title> Self-sovereign identity (SSI) provides a decentralized approach to digital identity management, enabling individuals to control their personal data without reliance on centralized authorities. Blockchain technology offers a tamper-resistant and distributed infrastructure that can support secure and verifiable identity systems. In health care, where identity fragmentation, privacy risks, and interoperability challenges persist, blockchain-enabled SSI (BC-SSI) has been proposed as a potential solution. However, existing research remains heterogeneous, with varying levels of technical maturity and limited evidence of real-world deployment. </sec> <sec> <title>OBJECTIVE</title> This study conducts a scoping review to systematically map BC-SSI applications in health care and to analyze their application domains, development stages, study aims, targeted challenges, and technological infrastructures. In addition, this study aims to identify structural gaps in current research and assess the readiness of BC-SSI systems for clinical deployment. </sec> <sec> <title>METHODS</title> This review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) methodology. A comprehensive literature search conducted between September 2024 and August 2025 identified 37 peer-reviewed studies that met predefined inclusion criteria. Data were extracted and synthesized using descriptive and thematic analyses across application areas, system maturity, technological components, and reported challenges. </sec> <sec> <title>RESULTS</title> The findings indicate that BC-SSI research in health care remains at an early stage of maturity, with most studies proposing conceptual models or prototype implementations and limited real-world validation. Applications predominantly focus on identity verification, credential management, and privacy-preserving data exchange across domains such as electronic health records, mobile health, and access control systems. Commonly used technologies include decentralized identifiers, verifiable credentials, smart contracts, and privacy-enhancing mechanisms such as zero-knowledge proofs and selective disclosure. Despite rapid technical development, persistent challenges include interoperability limitations, governance gaps, usability concerns, and insufficient integration with health care infrastructures. Notably, a structural gap was identified between technological capability and system-level readiness for clinical deployment. </sec> <sec> <title>CONCLUSIONS</title> BC-SSI technologies demonstrate potential for enabling secure, interoperable, and patient-centric identity management in health care. However, current research is predominantly technology-driven and lacks sufficient system-level validation. This study highlights the need for integrated architectural approaches, governance frameworks, and real-world evaluation to bridge the gap between conceptual innovation and clinical implementation. Advancing BC-SSI toward health care adoption will require coordinated progress across technical, organizational, and regulatory dimensions. </sec>
Abstract Federated learning (FL) has emerged as a paradigm-shifting approach to distributed machine learning, enabling multiple participants to collaboratively train models without exposing raw data. However, conventional federated learning architectures remain susceptible to a broad spectrum of security and privacy threats, including model poisoning, gradient inversion, inference attacks, and Byzantine faults. This paper presents a unified and robust framework— Secure and Privacy-Preserving Federated AI (SPFA) — that integrates differential privacy, homomorphic encryption, secure multi-party computation, Byzantine fault tolerance, and zero-knowledge proofs into a unified, production-grade architecture. We formally analyze the threat model, prove privacy guarantees under the ( ε , δ )-differential privacy framework, and demonstrate Byzantine resilience under partial adversarial participation. Extensive experiments on heterogeneous data distributions across image classification, natural language processing, and medical diagnosis benchmarks demonstrate that SPFA achieves model accuracy within 2.3% of centralized baselines while providing provable ε = 1.0 privacy with a communication overhead of only 18% above standard FedAvg. To the best of our knowledge, our framework is among the first to consolidate all five protection layers into a unified, deployable system with formal analysis and an open-source reference implementation. The relevance of SPFA extends to privacy-sensitive applications in healthcare, cybersecurity, distributed edge computing, and smart city analytics.
Introduction: Intelligent room systems are experiencing a surge in demand within the Healthcare 4.0 ecosystem. The integration of Federated Learning (FL) and Data-Centric AI has led to substantial enhancements in the predictive capabilities of machine learning models while maintaining data privacy. However, centralized aggregation in FL remains a single point of failure and is vulnerable to poisoning attacks. Methods: This paper presents a novel, privacy-preserving architecture for Ambient Intelligence (AmI) that integrates Distributed Ledger Technology (DLT). Results: We explicitly note that while DLT does not preemptively prevent the generation of poisoned gradients, it provides an immutable, cryptographically secure audit trail. This ensures the trustworthiness and traceability of model updates for post-hoc detection, strict accountability, and targeted model rollbacks. Discussion: By fusing Data-Centric AI for quality assurance with a Blockchain-enabled FL framework, we propose a scalable, low-cost solution for real-time patient monitoring in diverse economic settings.
With the growing volume of sensitive data stored and processed in cloud environments, conventional security models are no longer sufficient to guarantee privacy, integrity, and trust. This paper proposes a blockchain-based framework that integrates Zero-Knowledge Proofs (ZKPs) and homomorphic encryption (HE) to enable secure and privacy-preserving data sharing. ZKPs are employed to verify user access rights without exposing identities or underlying information, while HE allows computations to be performed directly on encrypted data, ensuring confidentiality is preserved throughout the data lifecycle. The proposed framework addresses the limitations of existing approaches that either lack encrypted computation capabilities or expose sensitive data during processing. Formal and informal analyses demonstrate the feasibility of the model in terms of encryption time, ZKP verification latency, and computation overhead. The framework is designed to be applied initially in the healthcare sector and aligns with national digital transformation initiatives such as Saudi Vision 2030.
The rapid aging of the global population necessitates automated healthcare environments, yet current Intelligent Room architectures relying on centralized cloud servers face critical challenges regarding data opacity and single points of failure. This paper proposes a novel architecture that synergizes Distributed Ledger Technology (DLT) with Federated Learning (FL) to create a trustless, immutable audit trail for patient monitoring. Unlike traditional FL approaches, we introduce a blockchain-based aggregation mechanism that eliminates the central authority. Furthermore, to address the resource constraints of edge devices such as smartphones, we implement a specific Lightweight Neural Network (L-CNN) utilizing depthwise separable convolutions. The proposed system ensures that patient data remains local while model updates are cryptographically verified on-chain, offering a scalable, low-cost solution for resource-constrained healthcare environments.
Healthcare AI systems put a lot of importance on keeping medical data private because it is very sensitive. AI-driven diagnostic models could help doctors make better decisions, but they need a lot of different patient data sets, which are often kept separate from each other at different hospitals. Federated Learning (FL) is a decentralised way to solve this problem by letting multiple people train a model together without sharing data in one place. But conventional FL frameworks continue to encounter challenges related to trust, transparency, and data integrity. This paper puts forth a Blockchain-Enabled Federated Learning Framework to facilitate secure, privacy-preserving, and auditable medical diagnosis across decentralised healthcare systems. This system uses blockchain's unchangeable nature and smart contract features to make sure that model updates can't be changed, contributions can be tracked, and trust between the entities involved is higher. This combination makes AI-driven diagnostics possible without putting patient privacy, regulatory compliance, or institutional integrity at risk.
Federated Learning (FL) is an approach that allows numerous users to train a single machine learning model with the oversight of a central server, and with their training data stored locally on their devices. The approach is relevant in alleviating the risks associated with violations in data privacy. It is a process by which a pool of clients collaborates towards solving machine learning problems, with a central coordinator being the one who coordinates the entire process. The paper will review the latest advances in privacy-preserving federated learning and discuss it in the context of machine learning. It assesses privacy-related solutions, which are already in existence, such as secure aggregation, meta-learning, blockchain technology, decentralized training, searchable encryption, and data privacy mechanisms and zero-knowledge proofs. Federated learning (FL) is an emerging technology that can be used in the realm of the intelligence of the Internet of Things. However, the information that is model-related can be shared in FL and reveal the sensitive data of the participants. In this regard, we propose a new privacy-preserving FL framework, which is founded on a new chained secure multiparty computing technique, which we call chain-PPFL. The scheme we are proposing is based mostly on two mechanisms: 1) a single-masking mechanism, which protects the information that is exchanged between participants in a serial chain frame and 2) a chained-communication mechanism, which allows the masked information to be communicated between participants in a serial chain frame. We run large-scale experiments with respect to simulation by comparing the training accuracy and the leak defence to other state-of-the-art schemes with two publicly available data sets (MNIST and CIFAR-100). We established data sample distributions (IID and NonIID), and training models (CNN, MLP and L-BFGS) in our experiments. The experiment results show that the chain-PPFL scheme can offer a realistic privacy preservation (which is the same as the various privacy with ϵ to near zero) to FL at the cost of communication, and without compromising the accuracy and convergence rate of the training model.