# Imitation Privacy

arXiv: https://arxiv.org/abs/2009.00442 · HTML: https://mlsanity.com/paper/2009.00442/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2020.

## Built on by (most cited first, 1 of 1)

- [GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations](https://mlsanity.com/paper/2106.01425/) (2021)

## Abstract (arXiv)

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.