A PRIVACY-PRESERVING AND SECURE MACHINE LEARNING FRAMEWORK

Abstract

Machine learning has become a key technology for intelligent decision-making in healthcare, finance, cloud computing, and IoT applications. However, conventional centralized machine learning models expose sensitive user data to privacy breaches, cyberattacks, and unauthorized access. This paper proposes a Privacy-Preserving and Secure Machine Learning Framework that integrates Federated Learning (FL), Differential Privacy (DP), Secure Multi-Party Computation (SMPC), encryption techniques, and secure model aggregation to enable collaborative model training without sharing raw data. The framework ensures data confidentiality, integrity, and secure communication while maintaining high prediction accuracy. Experimental evaluation demonstrates improved privacy protection, scalability, robustness, and resistance to malicious attacks compared with traditional approaches. The proposed framework provides a reliable, efficient, and scalable solution for secure machine learning in real-world distributed environments.