Fully Homomorphic based Privacy-Preserving Distributed Expectation Maximization on Cloud

Expectation maximization (EM) is a clustering-based machine learning algorithm that is widely used in many areas of science (e.g. bioinformatics and computer vision) to finding maximum likelihood and maximum a posteriori estimates for models with latent variables. To deploy such an algorithm in cloud environments, security and privacy issues need be considered to avoid data breaches/abuses by external malicious parties or even by cloud service providers (CSPs). However, the processing performance of the EM algorithm poses a challenge in terms of building a secure environment. This paper describes an innovative and practical privacy-preserving EM algorithm for cloud systems that addresses this challenge and it estimates the EM parameters in an accurate and secure manner. Fully homomorphic encryption (FHE) is used to ensure both the privacy of the EM algorithm computations as well as the users' sensitive data in the cloud. A distributed-based approach is also proposed to overcome the overheads of FHE computations and to ensure a fast convergence of the EM algorithm. The conducted experiments demonstrate a significant improvement in the convergence time of the distributed EM algorithm while achieving a high level of accuracy and reducing the associated computational FHE overheads.