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Split learning and inference propose to run training/inference of a largemodel that is split across client devices and the cloud. However, such a modelsplitting imposes privacy concerns, because the activation flowing through thesplit layer may leak information about the clients' private input data. Thereis currently no good way to quantify how much private information is beingleaked through the split layer, nor a good way to improve privacy up to thedesired level.  In this work, we propose to use Fisher information as a privacy metric tomeasure and control the information leakage. We show that Fisher informationcan provide an intuitive understanding of how much private information isleaking through the split layer, in the form of an error bound for an unbiasedreconstruction attacker. We then propose a privacy-enhancing technique, ReFIL,that can enforce a user-desired level of Fisher information leakage at thesplit layer to achieve high privacy, while maintaining reasonable utility.

Measuring and Controlling Split Layer Privacy Leakage Using Fisher Information