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Differentially Private Filtering

机译：差分专用过滤

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摘要

Emerging systems such as smart grids or intelligent transportation systems often require end-user applications to continuously send information to external data aggregators performing monitoring or control tasks. This can result in an undesirable loss of privacy for the users in exchange of the benefits provided by the application. Motivated by this trend, this paper introduces privacy concerns in a system theoretic context, and addresses the problem of releasing filtered signals that respect the privacy of the user data streams. Our approach relies on a formal notion of privacy from the database literature, called differential privacy, which provides strong privacy guarantees against adversaries with arbitrary side information. Methods are developed to approximate a given filter by a differentially private version, so that the distortion introduced by the privacy mechanism is minimized. Two specific scenarios are considered. First, the notion of differential privacy is extended to dynamic systems with many participants contributing independent input signals. Kalman filtering is also discussed in this context, when a released output signal must preserve differential privacy for the measured signals or state trajectories of the individual participants. Second, differentially private mechanisms are described to approximate stable filters when participants contribute to a single event stream, extending previous work on differential privacy under continual observation.
机译：诸如智能电网或智能运输系统之类的新兴系统通常需要最终用户应用程序将信息连续发送到执行监视或控制任务的外部数据聚合器。交换应用程序提供的好处后，这可能会导致用户不希望的隐私丢失。受这一趋势的推动，本文在系统理论的背景下介绍了隐私问题，并解决了释放尊重用户数据流隐私的过滤信号的问题。我们的方法依赖于数据库文献中的一种正式的隐私概念，即差异隐私，它为带有任意附带信息的对手提供了强有力的隐私保证。开发了通过差分私有版本来近似给定滤波器的方法，从而使由隐私机制引入的失真最小化。考虑了两种特定的情况。首先，差分隐私的概念扩展到动态系统，其中许多参与者贡献了独立的输入信号。当释放的输出信号必须为各个参与者的测量信号或状态轨迹保留差分保密性时，也将在本文中讨论卡尔曼滤波。其次，当参与者参与单个事件流时，描述了差分私有机制来近似稳定的过滤器，从而在持续观察下扩展了先前关于差分隐私的工作。

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