https://doi.org/10.26706/ijceae.6.3.20250608

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Abstract:-

Emerging, highly skilled cyberattacks demand novel and robust techniques for AI-powered privacy preservation. Centralized machine learning models can be compromised with a single point of failure, a data breach, or an adversarial attack. The proposed work presents a unique AI-based Privacy Shield that enhances Privacy-Aware Hybrid Privacy-Preserving Federated Learning (HPP-FL), Blockchain-Enhanced Secure Aggregation (BESA), and Quantum-Resistant Encryption (QRE-FL). By employing an Adaptive Adversarial Training (AAT) strategy, the defense mechanism adjusts to the transforming cyber threats in real-time, thus demonstrating prevention abilities. This approach allows multiple users to collaboratively train a global deep learning model securely with minimal bandwidth and without relying on any central aggregator, similar to federated learning but built on a blockchain-based secure aggregation protocol. Additionally, quantum-resistant encryption mechanisms provide an added layer of security against emerging threats posed by quantum computing, securing the future of federated models. The framework is validated on real-world data from the healthcare, finance, and IoT domains. It shows improvements of 91.2% accuracy, 40% less data leakage, and 35% more resistance to attacks, all while using little extra computing power. This makes it possible for AI security to be scalable and future-proof, making FL a more credible privacy-protecting option for real-world uses.

Index Terms:-

Federated Learning, Hybrid Privacy-Preserving FL, Blockchain, Quantum-Resistant Encryption.

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