Communication-Efficient Secure Logistic Regression

We present a new two party construction for secure training of logistic regression which enables two parties to process private data to build logistic models without learning anything more about the data than the designated output. Our construction improves the communication cost of the online work stage which is executed once the inputs become available. As part of our construction we develop many building blocks which are of independent interest including a new approximation technique for the sigmoid functionality which results in a secure evaluation protocol with better communication. Further, we develop secure spline evaluation and secure powers computation protocol for values with fixed-point representation as well as a new comparison protocol that optimizes online communication. We present a new two party computation construction for DPF keys that evaluate arithmetic shares of the function evaluation output over large rings, while previous construction do this only for boolean outputs. We benchmark the costs of our constructions and compare with previous works.

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