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arXiv · 2009.00442

Imitation Privacy

Xun Xian·Xinran Wang·Mingyi Hong·Jie Ding·Reza Ghanadan
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Abstract

In recent years, there have been many cloud-based machine learning services, where well-trained models are provided to users on a pay-per-query scheme through a prediction API. The emergence of these services motivates this work, where we will develop a general notion of model privacy named imitation privacy. We show the broad applicability of imitation privacy in classical query-response MLaaS scenarios and new multi-organizational learning scenarios. We also exemplify the fundamental difference between imitation privacy and the usual data-level privacy.

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Xun Xian, Xinran Wang, Mingyi Hong, Jie Ding, Reza Ghanadan. 2020-08-30. Imitation Privacy. https://arxiv.org/abs/2009.00442

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