DEEP-PRIVACY proposes a new paradigm based on a distributed, personalized, and privacy-preserving approach for speech processing, with a focus on machine learning algorithms for speech recognition. To this end, we propose to rely on a hybrid approach: the device of each user does not share its raw speech data and runs some private computations locally, while some cross-user computations are done by communicating through a server (or a peer-to-peer network). To satisfy privacy requirements at the acoustic level, the information communicated to the server should not expose sensitive speaker information. The project addresses the above challenges from the theoretical, methodological and empirical standpoints through two major scientific objectives.
The first objective is to learn privacy-preserving representations of the speech signal, that disentangle features exposing private information, to be kept on the user’s device (speaker- specific information) from generic features useful for the task of interest, to be shared with servers (phonetic / linguistic information).
The second objective concerns distributed algorithms and personalization, through the design of efficient distributed algorithms which operate under the setting where sensitive user data is kept on-device, with global components running on servers and personalized components running on personal devices.