Encrypted Machine Learning Models: Challenges and Opportunities

Abstract

The advent of machine learning (ML) has revolutionized numerous industries by enabling sophisticated data-driven decision-making processes. However, the widespread adoption of ML models raises significant concerns regarding data privacy and security. Encrypted machine learning models have emerged as a promising solution to mitigate these concerns. By encrypting models during training and inference stages, sensitive data remains protected from unauthorized access and adversarial attacks. This paper explores the challenges and opportunities associated with encrypted ML models, including computational overhead, performance degradation, and compatibility with existing frameworks. We discuss various encryption techniques, such as homomorphic encryption and secure multiparty computation, highlighting their strengths and limitations in practical implementations. Moreover, we examine current research trends and future directions aimed at enhancing the efficiency and scalability of encrypted ML models. Ultimately, this study underscores the pivotal role of encryption in advancing trustworthy and privacy-preserving machine learning applications in the era of ubiquitous data.