Is Private Learning Possible with Instance Encoding?

Nicholas Carlini; Samuel Deng; Sanjam Garg; Somesh Jha; Saeed Mahloujifar; Mohammad Mahmoody; Shuang Song; Abhradeep Thakurta; Florian Tramer

arXiv · 2011.05315

Is Private Learning Possible with Instance Encoding?

Nicholas Carlini·Samuel Deng·Sanjam Garg·Somesh Jha·Saeed Mahloujifar·Mohammad Mahmoody·Shuang Song·Abhradeep Thakurta·Florian Tramer

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Abstract

A private machine learning algorithm hides as much as possible about its training data while still preserving accuracy. In this work, we study whether a non-private learning algorithm can be made private by relying on an instance-encoding mechanism that modifies the training inputs before feeding them to a normal learner. We formalize both the notion of instance encoding and its privacy by providing two attack models. We first prove impossibility results for achieving a (stronger) model. Next, we demonstrate practical attacks in the second (weaker) attack model on InstaHide, a recent proposal by Huang, Song, Li and Arora [ICML'20] that aims to use instance encoding for privacy.

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Nicholas Carlini, Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Shuang Song, Abhradeep Thakurta, Florian Tramer. 2020-11-10. Is Private Learning Possible with Instance Encoding?. https://arxiv.org/abs/2011.05315

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