Achieving Privacy-Preserving and Verifiable Support Vector Machine Training in the Cloud

With the proliferation of machine learning, the cloud server has been employed to collect massive data and train machine learning models. Several privacy-preserving machine learning schemes have been suggested recently to guarantee data and model privacy in the cloud. However, these schemes either mandate the involvement of the data owner in model training or utilize high-cost cryptographic techniques, resulting in excessive computational and communication overheads. Furthermore, none of the existing work considers the malicious behavior of the cloud server during model training. In this paper, we propose the first privacy-preserving and verifiable support vector machine training scheme by employing a two-cloud platform. Specifically, based on the homomorphic verification tag, we design a verification mechanism to enable verifiable machine learning training. Meanwhile, to improve the efficiency of model training, we combine homomorphic encryption and data perturbation to design an efficient multiplication operation for the encryption domain. A rigorous theoretical analysis demonstrates the security and reliability of our scheme. The experimental results indicate that our scheme can reduce computational and communication overheads by at least 43.94% and 99.58%, respectively, compared to state-of-the-art SVM training methods.

I. Introduction

Recently, machine learning has been widely used in practice to provide predictive models for healthcare [1], identity authentication [2], and recommendation services [3]. Support vector machine (SVM), as one of the most popular machine learning frameworks, has garnered significant attention. The accuracy of SVM models depends on the size and diversity of the training data used. Therefore, collecting large and diverse data is essential for training SVM models. For example, internet companies collect large amounts of users’ browsing history, comments, bookmarks, and other online activities to train SVM models for the purpose of delivering intelligent, personalized recommendation services.