TL;DR
The proposed framework effectively integrates encryption, federated intrusion detection, explainable artificial intelligence, and blockchain security to enhance privacy, transparency, and reliability in IoMT healthcare networks.
Using federated learning (FL) in health care applications, the teams will be able to collaborate to predict the health of patients without exchanging any personally identifiable information. Regardless of this benefit, FL is still not widely used since it is susceptible to serious privacy and security risks like membership inference and model poisoning attacks. To address these concerns, this paper proposes a blockchain-enhanced FL model that involves safe aggregation and audit-trail immutability to thwart privacy leakage and manipulation of medical data analysis by opponents. Random Forest and LightGBM classifiers are trained on fake healthcare datasets in various scenarios of attack. Some of the security measures that are used to assess the system include attack success rate, cost of communication, differential privacy (DP) epsilon values and blockchain-based tamper detection delay. The proposed approach can achieve measurable privacy leaks reduction and a 40-percent lower success rate in membership inference attacks, as demonstrated in experiments. Also, blockchain audit trails can provide real-time resilience against tampering of data. According to these findings, blockchain-enhanced FL appears to be a promising framework of medical AI systems capable of ensuring the patient data safety and privacy.
The proposed framework for financial system fraud detection that is safe and protects privacy while resolving issues with data sharing, legal restrictions, and cybersecurity threats is appropriate for practical financial applications since it successfully improves fraud detection while guaranteeing Privacy Preservation, security, and openness.
The proposed hybrid approach outperforms ML-only and blockchain-only baselines, offering a scalable, secure, and real-time IDS for IIoT infrastructures.
The results indicate that the suggested framework can offer a scalable and privacy-aware security architecture for next-generation smart healthcare systems and must be treated as a simulation-based reference that needs to be validated against real healthcare datasets and deployment scenarios.
A block chain-based Privacy-preserving and Secure Federated Learning (BPS-FL) system that uses threshold homomorphic encryption to safeguard the local gradients of clients in order to successfully solve such privacy and security assault challenges is suggested.
This study examines the integration of Artificial Intelligence (AI) and blockchain technology as a transformative approach to healthcare data security, and explores the role of blockchain-secured federated learning, which enables collaborative model training across healthcare institutions without exposing sensitive patient data.
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