Privacy-Preserving Training of Support Vector Machines via Secure Multiparty Computation

The power and ubiquity of machine learning demand security measures for protecting sensitive data. Secure multiparty computation (MPC) techniques enable a group of parties to jointly compute a given function while keeping the information private. In this work, we engineer a prototype for privately training support vector machines (SVMs) using MPC techniques. We conduct an extensive study on how different approaches for training SVMs interact with existing state-of-the-art MPC protocols. We identify the least squares (LS) approach as the best suited for privately training. We then optimize fixed-point precision, ensuring accuracy while keeping low running time and communication. The technical details of the optimization involve bounds on the step size of a gradient method to solve a linear system, which might be of independent interest. We further propose and analyse different alternatives to improve the LS approach on an MPC implementation, and we compare their performance. The best improvement yields up to 2× reduction of the running time and communication complexity, without affecting the accuracy of the trained model. In order to illustrate the feasibility of our solution, we securely train SVMs for two realistic tasks.