Secure support vector machines outsourcing with random linear transformation

The support vector machine (SVM) is a popular classification algorithm. Since training the SVM is very time-consuming, outsourcing the computations of solving the SVM to external service providers benefits the data owner who is not familiar with the techniques of the SVM or has only limited computing resources. In outsourcing, the data privacy is a critical concern for some legal or commercial reasons since there may be sensitive information contained in the data. Existing privacy-preserving data mining works are either not appropriate to outsourcing the SVM or weak in security. In this paper, we propose a scheme for privacy-preserving outsourcing of the SVM, which perturbs the data by random linear transformation. The service provider solves the SVM from the perturbed data without knowing the actual content of the data, and the generated SVM classifier is also perturbed, which can only be recovered by the data owner. Both the privacy of data and generated classifiers are protected. The proposed scheme is stronger in security than existing techniques, and incurs very little additional communication and computation cost. The experimental results show that the proposed scheme imposes very little overhead on the data owner, and the classification accuracy is similar to a normal SVM classifier.