Aug 2026· Journal of King Saud University: Computer and Information Sciences· Vol 38· 0 citations· 40 references
TL;DR
An engineering framework for privacy-preserving federated threat intelligence sharing that integrates trust-aware robust aggregation with blockchain-based integrity anchoring is proposed that confirms the effectiveness of the proposed trust-aware aggregation mechanism in mitigating malicious updates while preserving stable convergence and reliable intrusion detection performance.
Abstract
Collaborative threat intelligence sharing has become essential for defending distributed enterprise and smart city infrastructures against increasingly sophisticated cyber threats. However, organizations remain reluctant to share raw security data due to privacy, regulatory, and trust concerns. Federated Learning (FL) has emerged as a promising solution by enabling collaborative model training without exposing local data. Nevertheless, traditional FL-based intrusion detection frameworks remain vulnerable to model poisoning, Byzantine attacks, and lack transparent accountability mechanisms for cross-organization collaboration. This paper proposes an engineering framework for privacy-preserving federated threat intelligence sharing that integrates trust-aware robust aggregation with blockchain-based integrity anchoring. The proposed architecture introduces a dynamic reputation mechanism that evaluates participant reliability across communication rounds and assigns adaptive aggregation weights to mitigate malicious updates. To enhance transparency and non-repudiation, model update hashes and trust evolution records are anchored on a permissioned blockchain through smart contracts, ensuring immutable auditability without exposing sensitive parameters. The framework is evaluated using a non-IID partition of the ToN-IoT dataset across multiple simulated organizations. Experimental results demonstrate significant robustness improvements under adversarial environments. Under Byzantine attacks with 20% malicious clients, the proposed trust-aware aggregation mechanism achieves approximately 95% Accuracy and 95% F1-macro, compared with approximately 89% obtained using conventional FedAvg. Furthermore, under highly adversarial conditions involving 40% malicious participants, the proposed framework maintains approximately 95% Accuracy and F1-macro, whereas FedAvg degrades to approximately 78% Accuracy and 77% F1-macro. These results confirm the effectiveness of the proposed trust-aware aggregation mechanism in mitigating malicious updates while preserving stable convergence and reliable intrusion detection performance.
A trust-based federated learning framework in which a smart contract enabled by blockchain oversees client registration, model update logging, hash-based integrity verification, trust score calculation, malicious node penalization, aggregation approval, and decentralised audit logging is proposed.
Shankar Thalla· International Journal of Lat...· 0 citations
This research reveals that federated learning by blockchain is a robust and scalable platform to enable privacy-preserving artificial intelligence in healthcare, finance, IoT, smart city, and industrial applications.
Arthi D, R. Anand, Palaniappan Sambandam et al.· International journal of com...· 0 citations
Smart cities increasingly depend on large-scale Internet of Things (IoT) infrastructures for traffic management, smart grids, and environmental monitoring. Ensuring data integrity, transparency, and privacy in such systems remains a major challenge because centralized platforms are vulnerable to manipulation, while conventional blockchain-based solutions suffer from scalability and confidentiality limitations. This study proposes TrustIoT-Chain, a privacy-preserving blockchain framework that integrates off-chain digital twins, cryptographic data commitments, zero-knowledge compliance verification, and a sharded blockchain architecture for scalable smart city monitoring. The objective of this work is to provide real-time verifiable IoT monitoring with strong privacy guarantees and high system throughput. Large-scale simulations with one million synthetic IoT events demonstrate that the proposed framework achieves up to 24,910 events/s throughput with an average verification latency of 410 ms using 16 shards. Energy consumption is reduced by approximately 45% compared with non-sharded blockchain systems with zeroknowledge proofs, while privacy leakage measured by mutual information decreases to 0.05 bits. The key novelty lies in the joint integration of the digital twins with the blockchain-based zero-knowledge auditing, and sharding for the smart city IoT systems. This approach enables transparent regulatory compliance verification without exposing the raw sensor data, offering the scalable, and privacy-aware foundation for the future smart city governance, and trusted IoT ecosystems.
Shrutika Khobragade, J. Bakal· 2026 7th International Confe...· 0 citations
Secure financial transactions require more than just an immutable record — they also demand privacy-preserving identity assurance (which enables secure, trusted and transparent communication), adaptive fraud intelligence (to detect fraudulent transactions), policy-aware execution (so organizations can set their own rules for data use), resilient consensus (enables multiple parties to agree on data use), and auditable records within a single low-latency pipeline. Current permissioned-blockchain solutions often have independent optimizations for authentication, access control, fraud detection, consensus and auditing; as such, these separate areas lead to fragmented security decision making, unnecessary disclosure, static endorsement policies and throughput–latency tradeoffs. The research presented here describes FinTrust-X, a cross-layer risk-adaptive permissioned blockchain architecture where the security state created by each layer is used to create the next. A Zero-Knowledge Context Adaptive Role and Trust Authentication System (ZK-CARTA) provides zero knowledge context adaptive role and trust authentication to enable verifiable credentials to be selectively disclosed based on user device/session context and dynamically authorize users to minimize identity exposure and privilege abuse. Users are provided authenticated evidence to feed a Temporal Graph Transformer (TRiG-FraudFormer) that models joint transactional, account, device, merchant, beneficiary and trust relationships to produce a calibrated fraud-risk assessment along with counter-factual explanations. Risk is converted into adaptive smart contract paths, confidence levels and endorsement requirements to minimize unnecessary verification overheads. Safety constrained reinforcement learning is applied in RA-BFTune to adaptively optimize batching, ordering and Byzantine fault tolerant consensus based on transaction risk and network-states. Continuous cryptographic audit evidence is produced in PQ-AuditTwin utilizing immutable provenance, Merkle verification and ML-DSA-based post-quantum signature generations. Feedback regarding changes/drift in previous layer inputs is returned to those layers. Targeted validation results show ROC-AUC values of .96-.98 and F1 values of .92-.95 were achieved in addition to achieving authentication times less than 30ms., 1500-2000 TPS, P95 response time < 700ms, and greater than a 90% reduction in unnecessary disclosure of sensitive data from users indicating significant improvements in confidentiality, fraud-resilience, authorization-efficiency, scalability and auditability when compared against multi-organization Fabric workloads that included injected fraud and Byzantine faults.
P. Govardhan· Journal of Intelligent Decis...· 0 citations
This paper introduces Data Communities as a novel paradigm for privacy-preserving, blockchain-enabled cooperative digital infrastructures, formalized within the Cooperative Digital Infrastructure (CDI) framework and formalizes privacy guarantees through an adversarial model encompassing classical, quantum, insider, and governance-level threats.
Gerardo Iovane· Electronics· 0 citations
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