Posthoc Privacy guarantees for neural network queries

Cloud based machine learning inference is an emerging paradigm where users query with their data to a service provider who runs a ML model on the data and returns back the answer. Due to increased concerns over data privacy, recent works have proposed using Adversarial Representation Learning (ARL) to learn a privacy-preserving encoding of sensitive user data before it is shared with an untrusted service provider. Traditionally, the privacy of these encodings is evaluated empirically as they lack formal guarantees. In this work, we develop a new framework that provides formal privacy guarantees for an arbitrarily trained neural network by linking its local Lipschitz constant with its local sensitivity. To utilize local sensitivity for guaranteeing privacy, we extend the Propose-Test-Release~(PTR) framework to make it tractable for neural network based queries. We verify the efficacy of our framework experimentally on real-world datasets and elucidate the role of ARL in improving the privacy-utility trade-off.