  Privacy-preserving machine learning is learning from sensitive datasets that
are typically distributed across multiple data owners. Private machine learning
is a remarkable challenge in a large number of realistic scenarios where no
trusted third party can play the role of a mediator. The strong
decentralization aspect of these scenarios requires tools from cryptography as
well as from distributed systems communities. In this paper, we present a
practical scheme that is suitable for a subclass of machine learning algorithms
and investigate the possibility of conducting future research. We present a
scheme to learn a linear least squares model across two parties using a
gradient descent approach and additive homomorphic encryption. The protocol
requires two rounds of communication per step of gradient descent. We detail
our approach including a fixed point encoding scheme, and one time random pads
for hiding intermediate results.
