Privacy-preserving outsourcing support vector machines with random transformation

Abstract

Outsourcing the training of support vector machines (SVM) to external service providers benefits the data owner who is not familiar with the techniques of the SVM or has limited computing resources. In outsourcing, the data privacy is a critical issue for some legal or commercial reasons since there may be sensitive information contained in the data. Existing privacy-preserving SVM works are either not applicable to outsourcing or weak in security. In this paper, we propose a scheme for privacy-preserving outsourcing the training of the SVM without disclosing the actual content of the data to the service provider. In the proposed scheme, the data sent to the service provider is perturbed by a random transformation, and the service provider trains the SVM for the data owner from the perturbed data. The proposed scheme is stronger in security than existing techniques, and incurs very little redundant communication and computation cost.