Posthoc Privacy guarantees for neural network queries

Abhishek Singh, Praneeth Vepakomma, Vivek Sharma, Ramesh Raskar

Keywords: data privacy, differential privacy, privacy preserving machine learning, adversarial learning

TL;DR: We present a framework for achieving formal privacy guarantees in adversarially trained ML models

Abstract: Cloud based machine learning inference is an emerging paradigm where users share their data with a service provider. 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 tradeoff.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics

Submission Guidelines: Yes

Please Choose The Closest Area That Your Submission Falls Into: Social Aspects of Machine Learning (eg, AI safety, fairness, privacy, interpretability, human-AI interaction, ethics)