Privacy-Preserving Machine Learning Cryptographic Techniques for Secure Data Analysis

Abstract

Machine learning models rely on sensitive personal data, creating tension between utility and privacy. Privacy-preserving machine learning aims to enable secure data analysis through cryptographic techniques. This chapter provides an overview of fundamental cryptographic primitives including secure multiparty computation, homomorphic encryption, differential privacy, and federated learning. The authors explain how these techniques allow collaborative model training and prediction without compromising data confidentiality. Example real-world applications in sectors like healthcare, finance, and public policy are presented. Hybrid approaches combining complementary cryptographic tools are outlined to improve efficiency, accuracy, and privacy. Finally, the authors examine emerging directions such as post-quantum security, trusted execution environments, and on-device learning.