论文标题
模仿隐私
Imitation Privacy
论文作者
论文摘要
近年来，已经有许多基于云的机器学习服务，通过预测API向用户提供了训练有素的模型。这些服务的出现激发了这项工作，我们将在其中制定一个名为Imitation隐私的模型隐私概念。我们在经典的查询响应MLAA场景和新的多组织学习方案中展示了模仿隐私的广泛适用性。我们还举例说明了模仿隐私与通常的数据级隐私之间的基本差异。
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.
