Advancements in Homomorphic Encryption for Machine Learning Applications

Abstract

Homomorphic encryption (HE) has emerged as a pivotal technology in addressing privacy concerns while enabling the use of machine learning (ML) on sensitive data. This paper explores recent advancements in HE tailored for ML applications, focusing on both theoretical developments and practical implementations. We review the evolution of HE schemes, emphasizing improvements in efficiency, scalability, and usability. Key challenges such as computational overhead and data size limitations are addressed through novel cryptographic techniques and optimizations. Furthermore, we discuss case studies where HE has been successfully integrated into ML workflows, showcasing its potential across diverse domains including healthcare, finance, and telecommunications. Finally, we outline future research directions aimed at enhancing the performance and applicability of HE in real-world ML scenarios.