Privacy-Preserving Machine Learning Techniques, Challenges And Research Directions

Abstract

As machine learning models become increasingly ubiquitous, ensuring privacy protection has emerged as a critical concern. This paper presents an in-depth exploration of privacy-preserving machine learning (PPML) techniques, challenges, and future research directions. We delve into the complexities of integrating privacy-preserving methodologies into machine learning algorithms, pipelines, and architectures. Our review highlights the evolving landscape of regulatory frameworks and the pressing need for innovative solutions to mitigate privacy risks. Moreover, we propose a comprehensive framework, the Phase, Guarantee, and Utility (PGU) model, to systematically evaluate PPML solutions, providing a roadmap for researchers and practitioners. By fostering interdisciplinary collaboration among the machine learning, distributed systems, security, and privacy communities, this paper aims to accelerate progress in PPML, paving the way for robust and privacy-preserving machine learning systems.