Homomorphic encryption has recently attracted attention as a key solution to preserve privacy in machine learning applications.

We study the problem of learning a real-valued function that satisfies the Demographic Parity constraint. It demands the distribution of the predicted output to be independent of the sensitive attribute. We consider the case that the sensitive attribute is available for prediction.

A binary logistic regression classifier was implemented inPyTorch (v1.4.0 ) and trained over the rcv1 dataset to illustrate the speed ups possible using a GPU (Nvidia Tesla V100) versus only multithreading (24 CPU threads using an Intel Xeon Gold 5118). Speedups were tested for both batch gradient descent (with a 0.001 learning rate) andL-BFGS.

There is no obvious way to apply them to the datasets we consider.