This paper proposes a novel consensus mechanism for blockchain networks, termed Distributed Proof-of-Work with Differential Privacy Constraints (DPPoW). The core objective is to integrate differential privacy guarantees into a Proof-of-Work (PoW) system, mitigating the privacy vulnerabilities inherent in traditional blockchain designs. The proposed mechanism introduces calibrated noise directly into the hashing process, carefully controlled to achieve a specified ε-differential privacy level. Furthermore, it incorporates a distributed key generation scheme to bolster security and prevent centralized control. The design addresses the growing concern about data traceability and potential deanonymization of participants within blockchain networks. Mathematical formulations detail the noise injection process and the key generation protocol, demonstrating the feasibility and effectiveness of the approach. The primary innovation lies in the synergistic combination of PoW's security features with differential privacy, offering a robust solution for privacy-conscious blockchain applications. The system aims to balance security, anonymity, and computational efficiency, a challenging endeavor currently unmet by existing blockchain technologies.
Federated learning (FL) has emerged as a promising paradigm for training machine learning models on decentralized data, offering enhanced privacy and reduced communication costs. However, the inherent distributed nature of FL introduces significant challenges regarding trust, security, and model accuracy. This paper presents a formal verification framework for FL systems, leveraging secure multi-party computation (SMPC) and formal verification techniques to rigorously analyze data flow and model updates. The framework aims to provide guarantees about privacy, security, and model accuracy, addressing the unique vulnerabilities present in FL architectures. We define a mathematical model of an FL system, incorporating key elements such as clients, servers, and communication protocols. This model is then subjected to formal verification, utilizing techniques like model checking and symbolic execution to identify potential security breaches and inaccuracies. The results demonstrate the feasibility and effectiveness of applying formal verification to FL, offering a robust approach to ensuring the trustworthiness of these systems. Key performance metrics, including privacy loss, communication overhead, and model accuracy deviations, are quantified and analyzed within the verification process. The framework contributes to the development of more reliable and secure FL applications, particularly in sensitive domains such as healthcare and finance.
Nadia Dahmani, Imen Ben Salem, Syed Muhammad Salman Bukhari
Abstract Air quality (AQ) related health risks are increasing globally, creating urgent demand for artificial intelligence (AI) systems that are privacy-preserving, transparent, and auditable. Although Federated Learning (FL), Distributed Ledger Technologies (DLT), and Explainable AI (XAI) are widely recognized as core components of trustworthy AI, existing research remains divided across technical and application domains. This study conducts a computational, multistage systematic review (SR) of scholarly literature records published between 2011 and 2025 using the Semantic Scholar Graph API with intersection and union search strategies. A total of 13,458 records were retrieved and refined into a research-grade corpus of 7,215 publications through DOI-based deduplication, abstract quality diagnostics, venue and publication type screening, and exclusion of non-research artifacts. We used synergy detection, bibliometric analysis, and semantic clustering to study how these technologies are combined and distributed across research domains. The semantic clustering was performed using Sentence-BERT embeddings and UMAP. The results reveal a major structural gap. Among the 6,942 technology-tagged publications in the final 7,215-paper corpus, only 25 studies were identified as triple-synergy candidates that jointly mention or report the combined use of FL, DLT, and XAI, accounting for approximately 0.4%. Among these 25 triple-synergy publications, only two studies (8%) addressed climate or environmental applications, whereas 23 studies (92%) focused on healthcare or general AI. Semantic analysis further categorizes the studies into four areas: environmental sensing, provenance, governance, and clinical risk modeling. This division creates a critical challenge for scalable deployment, cross-domain interoperability, and policy-ready AI systems for AQ-health decision-making. To address this gap, we propose the Green-AI-Trust Stack, a layered architectural framework that integrates three complementary pillars (FL for privacy-preserving distributed learning, DLT for verifiable data and model provenance, and XAI for interpretable inference and decision support within a unified AQI-health pipeline). The findings provide practical direction for researchers, policymakers, and practitioners by identifying key research gaps, methodological needs, and future opportunities for reliable, auditable, and explainable AQI-health AI systems.
Automated health insurance claim processing has become increasingly important as insurers rely on intelligent systems to handle growing claim volumes and automated decision support systems for claim transparency. However, existing models often suffer from limited interpretability, insufficient data security, and weak generalizability across diverse claim patterns. To address these challenges, this research proposes InsureFusionNet, a hybrid explainable structured ensemble framework integrating heterogeneous models of deep feature learning, uncertainty-aware prediction, interpretable boosting mechanisms, and high-performance gradient-boosting classifiers through a fusion strategy for robust health insurance claim approval classification. Explainable AI techniques, including SHAP and LIME, are incorporated to provide transparent justifications for automated claim approval decisions, thereby enhancing stakeholder trust and accountability. To ensure data privacy and security, the proposed framework integrates AES-256-GCM encryption for confidentiality, elliptic curve cryptography for secure authentication, and SHA-256 hashing for integrity assurance within a permissioned Hyperledger Fabric blockchain, enabling controlled access, tamper-resistant auditability, and trustworthy claim management. Experimental results demonstrate that InsureFusionNet achieves superior performance, attaining an accuracy of 97.24%, precision of 98.41%, recall of 95.89%, and F1-score of 97.11% compared to individual classifiers. Overall, the proposed framework offers a secure, transparent, and reliable solution for secure and explainable automated health insurance claim approval classification and contributes toward responsible and trustworthy AI deployment in healthcare systems.
This paper proposes a novel decentralized consensus algorithm designed to achieve Byzantine fault tolerance while simultaneously incorporating differential privacy mechanisms. The core idea is to leverage a blockchain-inspired architecture for transaction validation and agreement, enhanced with encrypted transactions and a randomized consensus protocol. Crucially, noise is injected into the consensus process to obscure individual contributions, thereby satisfying differential privacy constraints. The algorithm addresses the critical challenge of combining security and privacy in distributed systems, offering a robust solution for applications requiring both data integrity and user privacy protection. We formally define the algorithm, outlining the key components and their interactions, and analyze its properties regarding fault tolerance, privacy guarantees, and performance. The approach represents a significant step towards practical decentralized systems capable of handling malicious actors while respecting individual user data. The presented framework provides a foundation for future research and development in this increasingly important area.
Federated learning (FL) enables collaborative model training without centralizing raw training records, but it does not inherently provide verifiable model provenance, enforceable fairness policies, or auditable control over aggregation. This paper presents FairAI, a blockchain- and IPFS-enabled framework that treats each local model as a governed artifact linked to performance and group-fairness metrics, content identifiers, manifests, Groth16 evidence, and smart-contract decisions. Only models approved on-chain and subsequently retrieved and validated through their registered CIDs are eligible for aggregation. The primary real-data evaluation used the Adult and COMPAS datasets under IID and joint label/protected-group non-IID partitions, with ten paired seeds comparing standard FedAvg, post hoc fairness assessment, a pre-aggregation fairness policy gate, and FairFed. Under heterogeneous Adult data, the policy gate reduced the demographic-parity gap from 0.0273 to 0.0127, while accuracy decreased from 0.7740 to 0.7629. Under heterogeneous COMPAS data, the equalized-odds gap decreased from 0.2262 to 0.1226, while accuracy decreased from 0.6495 to 0.5809; the paired accuracy and equalized odds differences remained significant after Holm correction, with adjusted p-values of 0.0318 and 0.0491, respectively. Additional bounded experiments evaluated a small multilayer perceptron, policy threshold sensitivity, logical-client scaling, poisoning, coordinate-wise median aggregation, two native Kubo/IPFS peers, V2 Groth16 verification, and smart-contract overhead. Thirty valid V2 proofs were accepted, six inconsistent cases were rejected, and direct Solidity verification consumed 348,811 gas per measured transaction. A full-path false-metric experiment showed that the proof verifies threshold compliance and artifact binding for supplied values, but does not establish their correct derivation from private data. When an approved artifact became unavailable, FairAI cancelled the round before aggregation and published no global model.
Open access
Privacy-Preserving Technologies in Data
Ethics and Social Impacts of AI
Artificial Intelligence in Healthcare and Education
Blockchain-based credential verification provides durable integrity and decentralized validation, but the persistence of blockchain commitments can make premature disclosure of sensitive attributes difficult to reverse. This study developed a pre-commitment privacy risk intelligence framework that assessed potential information leakage before credential commitment and used the estimated risk to guide adaptive protection. Synthetic credential records and simulated attacker-side information were employed to characterize privacy exposure through uniqueness, re-identification susceptibility, cross-source linkage, attribute inference, and attributeinteraction effects. These indicators were transformed into predictive features and evaluated using machinelearning models for continuous privacy-risk estimation and categorical risk assessment, followed by an optimization stage that considered privacy reduction and retained utility. In the reported training experiment, the gradient-boosted regression model achieved a mean absolute error of 0.03290, root mean square error of 0.04645, and coefficient of determination of 0.84398. Logistic regression provided the strongest classification performance, attaining 85.63% accuracy, 81.33% recall, an F1-score of 82.44%, and a privacy false-negative rate of 18.67%. A subsequent evaluation of 1,100 records reduced the mean privacy-risk score from 0.81826 to 0.67337, corresponding to a 17.71% relative reduction, while maintaining a mean utility score of 0.84227. The findings indicated that pre-commitment privacy assessment could support risk-sensitive protection decisions while retaining substantial utility for verifiable credential processing.
Federated learning (FL) has emerged as an important distributed machine learning paradigm allowing many users to train a model together without sharing raw data. However, FL’s decentralized design makes it very susceptible to both Byzantine and coordinated model poisoning attacks, where a few malicious rounds of gradients are strategically inserted to reduce the overall integrity of the model. Even the existing Byzantine-resilient aggregation methods such as geometric median, Krum, and trimmed mean are vulnerable to sophisticated, coordinated poisoning attacks that leverage statistical gaps in outlier detection. In this article, we present FedSentinel, a novel Byzantine-resilient federated learning framework that combines cryptographic gradient attestation with adaptive trust-weighted aggregation to protect against coordinated model-poisoning attacks, which are among the most serious challenges. Three key innovations are introduced in FedSentinel Cryptographic Gradient Attestation Protocol (CGAP) that utilizes commitment schemes and zero-knowledge range proofs to ensure the integrity of the gradients and verify that submitted updates satisfy predefined norm and direction constraints; in the current protocol configuration, CGAP provides gradient integrity verification rather than full gradient confidentiality from the server Dynamic Trust-Weighted Robust Aggregation (DT-RoA): the computation and updating of per-client trust scores based on historical gradient consistency and cross-validation signals; and Coordinated Attack Detection Engine (CADE): based on spectral analysis of the gradient covariance matrices, it detects and isolates colluding Byzantine clients. By participating in up to 100 clients on CIFAR-10, CIFAR-100, FEMNIST, and Sentiment140 datasets, extensive experiments conducted under up to 30% Byzantine adversaries reveal that FedSentinel achieves 91.36% average global accuracy, whereas state-of-the-art defenses such as FLTrust, FLAME, RoFL, ShieldFL, and DnC achieve 83.83–86.94%. FedSentinel outperforms these defenses by 4.42–7.53% in terms of accuracy, while decreasing attack success rates by 53.2% under coordinated Byzantine backdoor attacks. The proposed framework offers a promising approach for federated learning that is verifiable and trustworthy in adversarial environments.
Miriam W. Kaara, Jael S. Wekesa, Michael W. Kimwele
The integrity and credibility of educational institutions worldwide are being undermined by the growing issue of academic certificate fraud. The public's trust in the educational system, the integrity of job opportunities, and the legitimacy of authentic credentials are all negatively impacted by academic dishonesty. The majority of academic credentialing systems, both centralized and blockchain-based, focus on academic credential validation and immutability, even though academic institutions have embraced technology to improve academic credentialing procedures. This suggests that their support for intelligent and private fraud detection is minimal. In this work, we suggest an integrated method that combines smart anomaly detection with Federated Learning (FL). This makes it possible for several organizations to build a model for detecting certificate fraud without disclosing private information to other organizations. Each institution's private information is retained and incorporated into a global model. This enables the framework to handle concerns about data ownership and privacy as well as regulatory compliance. The framework uses Extreme Gradient Boosting (XGBoost) to identify anomalies in metadata and Convolutional Neural Networks (CNNs) to detect visual forgeries in certificates. A decentralized node is used to train the models, which are merged using the Federated Averaging (FedAvg) algorithm. The federated model performs well in anomaly detection, according to the experiments conducted. When compared to conventional centralized approaches, it significantly reduces false positives and false negatives, with an accuracy of up to 94% and an AUC of 0.97. The model's findings imply that a federated learning approach would make it possible for institutions to detect fraud in a secure, scalable, and cooperative manner. In this regard, this framework offers a workable way to create digital credential systems that are more trustworthy and protect privacy. Key words: Federated Learning, Blockchain, certificate fraud detection, privacy preservation, distributed datasets, secure collaboration.
Aasem N. Alyahya, Muidh Awadh Algahtani, Amani Ibraheem, Naglaa F. Soliman · 8 authors
Industry 4.0 is evolving rapidly, 6G networks are emerging, and this has led to a dramatic increase in ultra-latency-critical, computationally demanding jobs in Industrial Internet of Things (IIoT) environments such as real-time digital twins, collaborative robots, and augmented reality-guided assembly. However, the conventional D2D-assisted mobile edge computing (MEC) systems suffer from the severe performance degradation due to the harsh factory propagation environment, severe security threats on the open D2D links, strict industrial data privacy requirements, selfish resource sharing behaviour, and frequent service migration due to device mobility. In this research, we propose a holistic secure task co-offloading system that integrates Reconfigurable Intelligent Surfaces (RIS), permissioned blockchain with smart contracts, and federated learning, into an integrated D2D-MEC architecture for the IIoT. A federated secure multi-armed bandit algorithm enables privacy-preserving decentralized decision-making without revealing sensitive industrial data. Blockchain records off-load transactions through immutable ledgers and enables smart-contract-based incentive enforcement. RIS renders unreliable wireless channels dynamic with minimum energy overhead. In scenarios with hostile and imprecise information, the collaborative design can lower the long-term cost of the system, including task latency, energy consumption, migration overhead, and blockchain transaction fees. The proposed framework, compared to state-of-the-art baselines, reduces the average job completion latency by 29.6%, energy consumption by 36.8%, and migration cost by 41.2%, as demonstrated by extensive trace-driven simulations in actual 6G-IIoT manufacturing scenarios. The experimental results also show the robust performance under the simulated willingness manipulation and poisoned federated updates. The permissioned blockchain architecture provides the architectural security against the Sybil attack and other trust-related threats.
The Moroccan Ministry of Justice aims to enhance the process of the judicial system. Through digitalization, given the sensitive information and the complexity of managing this volume of data, along with the multiple electronic materials exchanged, several challenges regarding the security, integrity, and confidentiality of personal data are presented that indicate difficulties in confirming authenticity. Using blockchain technology, the Ministry of Justice can exchange data and knowledge in a secure and transparent way. The goal of the proposed method is to automate the procedure for generating birth certificates to strengthen trust, security, and operational efficiency within the Moroccan judicial system.
A fixed-odds contract on short-horizon price direction has a positive expected value only when its win probability exceeds the break-even rate implied by the payout ratio. A deployable predictor must also produce signals at a sufficiently stable rate. We formulate this setting as selective prediction with a coverage constraint and combine a five-seed gradient-boosting ensemble over a 90-dimensional causal feature panel with daily adaptive quantile thresholds, each estimated from the preceding 14 to 28 days of model scores, with parameters selected on training data alone. Configurations are frozen after three chronological pseudo-out-of-sample folds and evaluated on a held-out period from 1 January to 10 June 2026, and the whole procedure is then repeated on a quarterly re-freezing cadence over seven successive windows. Across BTC and ETH at 5- and 10-min horizons, with a payout of 0.8 and a 55.56% break-even rate, the models execute 10.4 to 11.0 trades per day, and all four selective win rates exceed break-even. Under a dependence-aware block bootstrap, three of four remain significant, and within a 32-test confirmatory family, two survive Holm–Bonferroni correction. Coverage stays inside the operational band in 26 of 28 re-frozen windows. Compared under one execution protocol, a fixed calibration slice drifts out of band while a trailing window does not, and adaptive conformal inference (ACI) matches the proposed rule on coverage when its step size is tuned but not otherwise, whereas an outcome-driven conformal controller reduces coverage by more than an order of magnitude. The expected value is insensitive to exchange fees, which consume under 5% of the measured edge, and sensitive to the payout term. Under matched feature sets, training pools, and coverage, most of the apparent cross-asset difference does not persist. This paper presents a proof of concept for the framework rather than making any claim about cryptocurrency predictability.
Blockchain data is immutable and publicly visible a sensitive signature is placed on-chain, anyone can attempt to verify it. This openness may lead to unintended information exposure. The standard ring signature cannot fully address all the privacy, selective-verification and time -controlled disclosure requirements that arise in modern secure systems. The proposed algorithm presents a Blockchain-based Ring Signature with Multi-Designated Verifier and Zero-Knowledge Proof (BRSMDV-ZKP) enables a signer anonymously authenticate a transaction with a group of public keys while ensuring that only designated verifiers can verify the signature, The scheme incorporates a challenge–response mechanism, randomized commitments, and encrypted verifiers specific data to ensure signer anonymity, trace resistance, and verifier exclusivity. A Zero-Knowledge Proof (ZKP) is employed to prove correct decryption of the signature without revealing the verifier′s private key. The time-lock puzzle enforces a predefined delay, preventing early verification and enabling reward–penalty mechanisms for verifier compliance. This approach reduces the risk of key leakage, preserves privacy in decentralized systems and multi-party environments, and supports secure applications such as confidential e-voting, sealed-bid auctions, and legal document verification on blockchain platforms.
Abstract In the current digital era, the storage of electronic health records on centralized platforms presents significant integrity, privacy and security challenges. Further, access to this stored healthcare data should be quick and efficient, especially during emergencies. Blockchain and edge computing brought a great revolution in managing healthcare data by ensuring security, immutability, and decentralized data sharing with reduced latency. But, the integration of edge computing with the blockchain networks is still a gap to achieve ideal healthcare goals of data security with real-time data processing. The contribution of this work is two-fold. First, a novel deep reinforcement learning based medical data offloading scheme is proposed for offloading healthcare data to the nearby edge servers from the end users. The learning policy uses the proximal policy optimization algorithm for making the optimal offloading decision and minimizes the overall delay and energy consumption of healthcare devices and edge servers. Second, we proposed a secure, scalable, and consent-based data sharing scheme among multiple stakeholders such as patients, hospitals, doctors, healthcare research institutes etc. The EHR sharing scheme uses the AES and RSA algorithms for encryption, which ensures only authorized and consent-based access to the sensitive data stored in IPFS. The performance of the proposed offloading scheme is evaluated in terms of delay and energy consumption whereas data sharing scheme is evaluated in terms of latency and throughput using Hyperledger Besu and Hyperledger Caliper platforms. The experimental study exhibits that the proposed approach is both feasible and scalable, making it suitable for integration into the e-healthcare systems.
Wang Lei, Jasni Mohamad Zain, Nur Atiqah Sia Abdullah, Marina Yusoff · 7 authors
The proliferation of Internet of Medical Things devices within the predictive healthcare paradigm necessitates robust, privacy-centric collaborative learning frameworks to detect and mitigate rapid clinical deterioration. Traditional federated learning methodologies, while attempting to preserve patient data locality, are fundamentally constrained by multi-round gradient synchronization protocols, imposing prohibitive communication latency and remaining susceptible to false negatives under extreme non-independent and identically distributed conditions. To address these challenges, this study introduces the Feature-Augmented Analytic Federated (FaFL) Architecture, which fundamentally replaces iterative gradient synchronization with a single-round closed-form computational paradigm. By instituting a proactive feature mixing mechanism via a decoupled zero-knowledge proof global buffer, the proposed framework empowers local grassroots nodes to neutralize extreme clinical heterogeneity in a single phase. The architecture employs a closed-form analytic solution combined with a trace-weighted absolute aggregation protocol to rigorously guarantee stochastic convergence and absolute cryptographic resilience without requiring recursive parameter exchanges. Extensive empirical evaluations against existing baselines under severe Dirichlet non-independent and identically distributed conditions and Byzantine poisoning attacks demonstrate that the framework fundamentally eradicates high false-negative rates in resource-constrained clinics. Consequently, the proposed architecture robustly guarantees generalization stability, substantially outperforms existing paradigms in predictive fidelity and computational efficiency, and establishes a new operational standard for mission-critical clinical networks.
The high aggregation of user relationship and behavioral data in social networks continues to aggravate privacy leaks. How to strike a balance between privacy protection and data availability has become a research hotspot. To collaboratively optimize user information security and community structure identification, this study proposes a social network privacy protection model that integrates differential privacy technology and community discovery algorithms. First, a differential privacy noise injection mechanism is constructed to perturb node data and combine it with blockchain storage to ensure that the data cannot be tampered with. Then, a community division strategy based on information entropy and mutual information is introduced to achieve high-precision community identification through modularity optimization. The accuracy of the proposed model reached 98.1% when the data set size was 800, which was about 3.4% and 9% higher than that of other models, respectively. The root mean square error was 8.2, which was about 20% lower than that of the traditional model. The convergence speed was increased to 380 iterations, which was about 15% faster than that of the comparison algorithm. The privacy protection strength and scalability scores reached 9.3 and 9.5, respectively. The simulation test results showed that, under different data types, the accuracy of the model grew from 0.87 to 0.98, and the F1 value grew from 0.84 to 0.95. The integration of differential privacy and community discovery effectively improves the privacy protection strength and structural analysis accuracy of social networks, providing a highly feasible solution for multi-scenario social data security analysis.
This paper presents a comprehensive study on integrating Deep Learning (DL) modelling Long Short-Term Memory (LSTM)-based models with blockchain technology to deal with the most critical problems in healthcare data management, security and analytics. Escalating the size of healthcare data exponentially due to the development of e-HRs (electronic health records), wearables, and real-time monitoring systems pushed traditional data storage and processing practices into the limelight as their most significant weaknesses. LSTM networks are perfect for analyzing time-series data in health care, such as disease classification, anomaly detection, and patient outcome prediction over the long run. Nevertheless, these models require sound data protection techniques and privacy measures to be followed per the regulations while maintaining trust. Blockchain technology fills in the gaps beyond LSTM by offering a decentralized, tamper-proof platform to safely store and share data, keeping confidentiality, integrity, and availability simultaneously. This paper surveys the available literature on hybrid models by flushing out the topic with the help of LSTM and blockchain. It explores their potential use in real-time healthcare analytics applications, along with the challenges of scalability and interoperability. By presenting a model through the use of these technologies, the research centres on sharpening health information systems such as accuracy, security, and transparency, which in turn intensify the trust of both the patients and the providers of care, thus enabling the development of a patient care solution that is more reliable and efficient.
PARI is a recent SNARK based on equifficient polynomial commitments, giving an exceptionally compact proof of just 1280 bits over the BLS12-381 curve, which is the smallest among all the known SNARKs in the literature. However, PARI does not achieve the zero-knowledge property; despite being very efficient, it is therefore less suitable for applications requiring witness privacy. In this work, we propose a zero-knowledge extension of PARI making it ideal for privacy-centric applications yet keeping the proof size compact. We prove perfect completeness, perfect zero-knowledge in the random-oracle model with challenge space <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>𝔽</mml:mi> <mml:mi>⧵</mml:mi> <mml:mi>K</mml:mi> </mml:mrow> </mml:math> , and knowledge soundness in the algebraic group model with random oracles under the SDH assumption.
Abstract The rapid digitization of healthcare has brought Electronic Health Records (EHRs) to the forefront of clinical data management; however, persistent challenges of centralized control, privacy breaches, absence of patient data ownership, and the inability to support decentralized scientific collaboration continue to impede scalable healthcare research ecosystems. Recent advances in Decentralized Science (DeSci) introduce a paradigm shift by leveraging blockchain, cryptographic primitives, and decentralized governance to enable transparent, trust-minimized, and collaborative biomedical research. This paper proposes a DeSci-driven lightweight hybrid blockchain framework designed to support privacy-preserving and incentive-aware decentralized healthcare research infrastructure. The framework integrates a permissioned blockchain with a lightweight hybrid PBFT–PoA consensus protocol, off-chain storage, and Zero-Knowledge Proof (ZKP)-based authentication to enable secure, privacy preserving data access without disclosing user identity. A tokenomics-based DAO governance layer is incorporated to support decentralized engagement, transparent policy enforcement, and incentive-driven research participation. The proposed system is evaluated through simulation under varying network conditions, with key performance metrics — latency, throughput, and computational cost — assessed across network sizes from 10 to 50 nodes. Simulation-based projections suggest that the proposed framework may achieve lower latency, higher throughput, and improved computational efficiency relative to literature-reported values for MedRec, FHIRChain, and HealthChain under the stated modeling assumptions; these comparisons are model-based and illustrative rather than measurements obtained from a controlled, identical-environment deployment. Beyond data management, the framework enables a DeSci-oriented research lifecycle encompassing decentralized data contribution, validation, and provenance tracking. The simulation-only nature of the current evaluation is explicitly acknowledged as a limitation, with a clear roadmap toward prototype-level implementation on Hyperledger Fabric or Ethereum as immediate future work.
This paper presents the Auditable Zero-knowledge Transformer (AZT) framework for privacy-preserving and auditable tax fraud detection. AZT combines transformer-based anomaly detection with zero-knowledge proof (ZKP) verification so that a tax authority or regulator can verify fraud-detection outcomes without accessing sensitive taxpayer records or proprietary model parameters. The framework is scalable in the specific sense of low-latency audit verification: proof verification remains sub-second, whereas proof generation is intentionally performed asynchronously after local inference. Model integrity is enforced through Merkle-root commitments to authority-approved parameters, and the ZKP statement proves that the committed transformer was executed correctly and that the resulting risk score satisfies a public audit threshold. Experiments on UCI-TFD, IRS-Pub, and CorpPay compare AZT with classical machine-learning baselines, including Random Forest and XGBoost, and with an equivalent plaintext transformer. Detection quality improves by up to 5.3% in F1-score over classical machine-learning baselines, while the circuit-compatible AZT inference incurs only about 0.5% F1-score degradation relative to the plaintext transformer baseline. Overall, this work advances secure AI for digital governance by integrating modern deep learning with cryptographic verification, offering a practical foundation for fraud-detection systems in which transparency and confidentiality must be satisfied simultaneously.
The Wireless Sensor Networks (WSN) and the Internet of Things (IoT) have revolutionized various application areas such as smart cities, health, industrial automation, environment, agriculture, and intelligent transportation systems. Despite the successful widespread use of WSNs-IoT, they still have several security issues including resource constrained sensor nodes, decentralized design, and the combination of heterogeneous communication protocols and insecure wireless communication channels. Most traditional security solutions including cryptographic methods, intrusion detection systems are rule-based, which is not enough to protect against the advanced, evolving and zero-day attacks. Therefore, the paradigm of artificial intelligence (AI) has become an exciting approach to creating intelligent, adaptive and autonomous cyber security solutions. This paper is a systematic literature review of the security solutions based on artificial intelligence (AI) applied to WSNs (WSNs) in the context of IoT. Structured review methodology is followed in the study, which critically analyzes recent machine learning, deep learning, reinforcement learning, federated learning, blockchain and edge intelligence advancements in the field of intrusion detection, anomaly detection, threat prediction, authentication, privacy preservation, and secure communication. These approaches are compared on the basis of their accuracy to detect the target, computational complexity, energy efficiency, scalability, and privacy and feasibility for deployment in resource constrained environments. Moreover, it classifies the already known security threats, examines layer-wise defense measures and analyzes upcoming hybrid AI frameworks, which combine several intelligent technologies. The review reveals several gaps in the research, such as the lack of explainability of models, use of benchmark datasets, susceptibility to adversarial and model-poisoning attacks, blockchain scalability issues, and the absence of standardized, secure system architectures that can offer reliable, privacy-preserving, and energy-efficient protection. The paper then proposes future research directions that highlight the need of combining Explainable Artificial Intelligence (XAI), Federated Learning, Blockchain, and Edge AI for the construction of strong and adaptive cybersecurity frameworks. This review is a comprehensive reference for researchers and practitioners who are interested in designing secure, intelligent and sustainable WSN-IoT systems for next-generation cyber-physical ecosystems.
Blockchain can secure and verify electronic health records (EHRs) for multi-institution healthcare systems, but Layer-1 storage costs and throughput limitations make full on-chain EHR storage impractical. A new model is proposed named FZRP (Federated-ZK-Rollup Pipeline). It is a hybrid methodology combining Federated Learning (FL), off-chain storage (IPFS), zk-rollup batching with adaptive batch sizing, and parallel proof pipelines to minimize per-record transaction cost while preserving auditability and privacy. Using a synthetic dataset of 50,000 EHRs, it quantifies cost reductions under realistic assumptions and demonstrate orders-of-magnitude per-record savings. A formal cost model, latency and security analyses, and sensitivity studies are provided. The experimental evaluation demonstrates that adaptive batching significantly reduces per-record transaction cost under conservative Layer-1 cost assumptions to as low as $0.000024, achieving over 99.999% cost reduction while maintaining scalability and privacy. The limitations, regulatory considerations, and paths for future work are discussed.