Balancing innovation and privacy : A machine learning perspective

Abstract

As digital innovation advances, concerns surrounding privacy escalate. This manuscript explores the intricate relationship between machine learning and privacy preservation. Beginning with a comprehensive literature review, we delve into the current state of privacy in the digital age and examine machine learning’s role in addressing these concerns. The manuscript highlights key privacy-preserving techniques, including homomorphic encryption, differential privacy, and federated learning, providing in-depth insights into their applications and real-world implementations. Anticipating future challenges and trends, we recommend maintaining a delicate equilibrium between innovation and privacy in the dynamic landscape of machine learning.