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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. |
