Systematic Literature Review on Differential Privacy in Machine Learning

With the rapid advancement of Machine Learning (ML) and its widespread applications in various domains, concerns over data privacy and security have become increasingly critical. Differential Privacy (DP) has emerged as a rigorous mathematical framework for privacy-preserving data analysis in ML systems, offering formal guarantees for protecting individual privacy while enabling meaningful learning. Previous surveys have lacked extensive coverage of DP and ML, failing to address the trade-offs between privacy and accuracy. Consequently, achieving a comprehensive understanding of the design, implementation, and efficiency of the DP algorithms within the ML domain is imperative. This survey provides a systematic review of DP methods across ML approaches, including traditional ML, federated learning, and deep learning. Through a thematic analysis of 106 studies, we identify key DP implementation strategies, examine their impact on model performance, and highlight the advantages and limitations of existing approaches. Our findings offer practical insights to assist researchers and practitioners in selecting appropriate DP mechanisms based on specific requirements. Finally, we discuss open challenges and future research directions to advance DP techniques for improved privacy-utility trade-offs in ML applications.