Privacy-Preserving Classification Scheme Based on Support Vector Machine

Abstract

As a classifier, support vector machine (SVM) explains a core problem of machine learning, namely sample classification in statistical terms. It has been widely used in machine learning, data mining, pattern recognition, and other fields. With the wide applications of SVMs in machine learning and big data, privacy protection of sensitive data in SVMs is becoming more and more important, such as face recognition and biometric information. At present, the main privacy protection methods in SVMs are homomorphic encryption and secure multiparty computation. However, there are some problems in current research. The computational efficiency is low, and the scalability of the schemes is poor. In addition, the user must stay online in some solutions. To solve the abovementioned problems, this article designs a secure and efficient classification scheme based on SVM to protect the privacy of private data and support vectors in the calculation and transmission process. First, the distributed two trapdoors public-key cryptosystem proposed by Liu is used to realize the distributed double-key decryption function, weaken the decryption capability of a cloud server with the master key, and prevent the server from launching active attacks. Second, we design a universal secure computing protocol for nonlinear SVMs based on the Gaussian kernel function, which can be extended to polynomial kernel function, sigmoid kernel function and is suitable to different kernel functions. Compared with existing schemes, our solution reduces the amount of encrypted data, simplifies the calculation process, and improves calculation efficiency. Third, an introduced cloud server realizes user offline function. Finally, we analyze the security of the scheme and verify its efficiency through experiments. Analysis and experimental results show that the scheme has the advantages of high efficiency, good scalability, and user offline function.