Bounding the Invertibility of Privacy-Preserving Instance Encoding Using Fisher Information

Neural Information Processing Systems 

In this paper, we propose a theoretically-principled measure for the invertibility of instance encoding based on Fisher information that is broadly applicable to a wide range of popular encoders.

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