Training Support Vector Machines with privacy-protected data

In this paper, we address a machine learning task using encrypted training data. Our basic scenario has three parties: Data Owners, who own private data; an Application, which wants to train and use an arbitrary machine learning model on the Users' data; and an Authorization Server, which provides Data Owners with public and secret keys of a partial homomorphic cryptosystem (that protects the privacy of their data), authorizes the Application to get access to the encrypted data, and assists it in those computations not supported by the partial homomorphism. As machine learning model, we have selected the Support Vector Machine (SVM) due to its excellent performance in supervised classification tasks. We evaluate two well known SVM algorithms, and we also propose a new semiparametric SVM scheme better suited for the privacy-protected scenario. At the end of the paper, a performance analysis regarding the accuracy and the complexity of the developed algorithms and protocols is presented.