SAP SE
ADVERSARIAL LEARNING OF PRIVACY PRESERVING REPRESENTATIONS

Abstract:

A first machine learning model may be trained to generate a first representation of a first user data having private attributes and non-private attributes. The first representation may be generated to achieve a focal entropy by maximizing an entropy amongst similar private attributes. The first representation to preserve information associated with the non-private attributes but omit information associated with the private attributes. Moreover, the first user data may be classified based on a target portion of the first representation including the non-private attributes but not the residual portion of the first representation including the private attributes. The trained first machine learning model may be applied to generate a second representation of a second user data such that downstream tasks may be performed by applying a second machine learning model to the second representation of the second user data. Related systems and computer program products are also provided.