Résumé
In an ever more data-centric economy, machine learning models have risen in importance. With the large amounts of data companies collect, they are able to develop highly accurate models to predict the behaviours of their customers. It is thus important to safeguard the data used to build these models to prevent competitors from mimicking their services. In addition, as this type of techniques finds its way into areas that need to deal with more sensitive information, like the medical industry, the privacy of the data that needs to be classified also has to be ensured. Herein, this topic is addressed by homomorphically evaluating Support Vector Machine (SVM) models, in a way that guarantees that a client learns nothing about the model except for the classification of his data, and that the service provider learns nothing about the data. Whereas, previously, Fully Homomorphic Encryption (FHE) has mostly focused on either bit-wise or value-wise computations, SVMs present an additional challenge since they combine both: during an initial phase a kernel function is evaluated that makes use of real arithmetic, and during a second phase the sign bit has to be extracted. Novel techniques are herein proposed that allow for speedups of up to 2.7 and 6.6 for the evaluation of polynomials and the determination of sign, respectively, in comparison to the state of the art. Finally, it is shown that the proposed techniques do not deteriorate the classification accuracy of the SVM models.