A Comprehensive Analysis of Privacy-Preserving Techniques in Machine Learning

With the widespread adoption of machine learning (ML) across various industries, safeguarding data privacy has become increasingly vital. This paper presents a comprehensive review of privacy-preserving techniques in ML, focusing on methods that protect sensitive information while maintaining strong model performance. The paper covers key techniques such as homomorphic encryption (HE), differential privacy, federated learning, and secure multi-party computation. By integrating recent research and developments, it assesses the effectiveness, challenges, and practical applications of these techniques. The paper also examines real-world use cases and emerging trends, offering insights into the balance between privacy, security, and computational demands, making it a valuable resource for both researchers and practitioners seeking to enhance privacy in ML applications.