  The foreseen growing role of outsourced machine learning services is raising
concerns about the privacy of user data. Several technical solutions are being
proposed to address the issue. Hardware security modules in cloud data centres
appear limited to enterprise customers due to their complexity, while general
multi-party computation techniques require a large number of message exchanges.
This paper proposes a variety of protocols for privacy-preserving regression
and classification that (i) only require additively homomorphic encryption
algorithms, (ii) limit interactions to a mere request and response, and (iii)
that can be used directly for important machine-learning algorithms such as
logistic regression and SVM classification. The basic protocols are then
extended and applied to feed-forward neural networks.
