Privacy-Preserving Machine Learning Techniques: Balancing Utility and Data Protection

Abstract

This research article provides information regarding the concerns of Privacy-Preserving Machine Learning (PPML) Techniques that includes Differential Privacy, Federated Learning, and Secure Multi-Party Computation. It is also noticed that Privacy Models are useful to achieve significant privacy and minimal loss in model accuracy. This further demonstrates that these kinds of strategies in the main application areas such as Healthcare, Banking, Internet-Of-Things (IOT), and Manufacturing, provides near-perfect privacy that can be useful while it meets minimal compromise in model accuracy. Some of the findings are enhanced data security, high accuracy of the chosen model, and ideas for future development.