A Recommender System for Efficient Implementation of Privacy Preserving Machine Learning Primitives Based on FHE

Abstract

With the increased dependence on cloud computing, there is growing concern for privacy of data that is stored and processed on third party cloud service providers. Of many solutions that achieve privacy preserving computations, fully homomorphic encryption (FHE) is a promising direction. FHE has several applications that can be used to perform computations on encrypted data without decrypting them. In this paper, we focus on realizing privacy preserving machine learning (PPML) using FHE. Our prime motivation behind choosing PPML is the increased use of machine learning algorithms on end-user’s data for predictions or classification, where privacy of end-user’s data is at stake. Given the importance of PPML and FHE, we formulate a recommender system that enables machine learning experts who are new to cryptography to efficiently realize a machine learning application in privacy preserving manner. We formulate the recommender system as a multi objective multi constraints optimization problem along with a simpler single objective multi constraint optimization problem. We solve this optimization using TOPSIS based on experimental analysis performed on three prominent FHE libraries HElib, SEAL and HEAAN from the PPML perspective. We present the observations on the performance parameters such as elapsed time and memory usage for the primitive machine learning algorithms such as linear regression and logistic regression. We also discuss the technical issues in making the FHE schemes practically deployable and give insights into selection of parameters to efficiently implement PPML algorithms. We observe that our estimates for matrix multiplication and linear regression correlate with the experimental analysis when assessed using an optimizer. The proposed recommendation system can be used in FHE compilers to facilitate optimal implementation of PPML applications.