  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.
