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Privacy-preserving instance encoding aims to encode raw data as featurevectors without revealing their privacy-sensitive information. When designedproperly, these encodings can be used for downstream ML applications such astraining and inference with limited privacy risk. However, the vast majority ofexisting instance encoding schemes are based on heuristics and theirprivacy-preserving properties are only validated empirically against a limitedset of attacks. In this paper, we propose a theoretically-principled measurefor the privacy of instance encoding based on Fisher information. We show thatour privacy measure is intuitive, easily applicable, and can be used to boundthe invertibility of encodings both theoretically and empirically.

Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Information