  Distributed devices such as mobile phones can produce and store large amounts
of data that can enhance machine learning models; however, this data may
contain private information specific to the data owner that prevents the
release of the data. We wish to reduce the correlation between user-specific
private information and data while maintaining the useful information. Rather
than learning a large model to achieve privatization from end to end, we
introduce a decoupling of the creation of a latent representation and the
privatization of data that allows user-specific privatization to occur in a
distributed setting with limited computation and minimal disturbance on the
utility of the data. We leverage a Variational Autoencoder (VAE) to create a
compact latent representation of the data; however, the VAE remains fixed for
all devices and all possible private labels. We then train a small generative
filter to perturb the latent representation based on individual preferences
regarding the private and utility information. The small filter is trained by
utilizing a GAN-type robust optimization that can take place on a distributed
device. We conduct experiments on three popular datasets: MNIST, UCI-Adult, and
CelebA, and give a thorough evaluation including visualizing the geometry of
the latent embeddings and estimating the empirical mutual information to show
the effectiveness of our approach.

Machine Learning;Computers and Society;Machine Learning