SAP SE
PRIVACY-PRESERVING REPRESENTATION MACHINE LEARNING BY DISENTANGLEMENT

Abstract:

In an example embodiment, a solution is provided to learn representations of a dataset in order to minimize the amount of information which could be revealed about the identity of each client. Specifically, one goal is to enable the system to learn relevant properties (e.g., regular labels that are non-privacy infringing) of a dataset as a whole while protecting the privacy of the individual contributors (private labels, which can identify a client). The database may be held by a trusted server that can learn privacy-preserving representations, such as by sanitizing the identity-related information from a latent representation.