Privacy Preserving Machine Learning: Secure Training and Deployment of Models on Sensitive Data

Abstract

Privacy-Preserving Machine Learning (PPML) is essential for securing sensitive data during model training and deployment. This paper proposes an integrated framework combining Federated Learning (FL), Differential Privacy (DP), Secure Multi-Party Computation (SMPC), and Homomorphic Encryption (HE) to enhance data privacy while maintaining model utility. FL enables decentralized training without data sharing, DP ensures individual data anonymity, SMPC facilitates collaborative learning without exposing private data, and HE allows computations on encrypted data. We analyze the computational complexity, privacy guarantees, and model effectiveness through empirical evaluation. Results demonstrate that FL and DP achieve an optimal balance of privacy and accuracy, while HE ensures maximum security at a higher computational cost. Privacy audits confirm strong resilience against membership inference and model inversion attacks. The study also highlights practical limitations and trade-offs, offering insights into scalable and efficient deployment of PPML techniques in healthcare, finance, and IoT applications.