Homomorphic Encryption for Machine Learning Applications with CKKS Algorithms: A Survey of Developments and Applications

Due to the rapid advancement of information technology, data has emerged as the core resource driving decision-making and innovation across all industries. As the foundation of artificial intelligence, machine learning(ML) has expanded its applications into intelligent recommendation systems, autonomous driving, medical diagnosis, and financial risk assessment. However, it relies on massive datasets, which contain sensitive personal information. Consequently, Privacy-Preserving Machine Learning (PPML) has become a critical research direction. To address the challenges of efficiency and accuracy in encrypted data computation within PPML, Homomorphic Encryption (HE) technology is a crucial solution, owing to its capability to facilitate computations on encrypted data. However, the integration of machine learning and homomorphic encryption technologies faces multiple challenges. Against this backdrop, this paper reviews homomorphic encryption technologies, with a focus on the advantages of the Cheon-Kim-Kim-Song (CKKS) algorithm in supporting approximate floating-point computations. This paper reviews the development of three machine learning techniques: K-nearest neighbors (KNN), K-means clustering, and face recognition-in integration with homomorphic encryption. It proposes feasible schemes for typical scenarios, summarizes limitations and future optimization directions. Additionally, it presents a systematic exploration of the integration of homomorphic encryption and machine learning from the essence of the technology, application implementation, performance trade-offs, technological convergence and future pathways to advance technological development.