Privacy in Control and Dynamical Systems
Privacy in Control and Dynamical Systems
复制标题
控制和动力系统中的隐私
DOI：
10.1146/annurev-control-060117-105018
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
George Pappas
中科院分区：
文献类型：
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作者：
Shuo Han;George Pappas
许多现代动态系统，如智能电网和交通网络，都依赖于用户数据来实现高效运行。这些数据通常包含参与用户不希望向公众透露的敏感信息。一个主要挑战是在使用用户数据时保护参与用户的隐私。在过去的十年中，差分隐私已经成为一种数学上严格的方法，提供了强大的隐私保证。特别是，差分隐私具有几个有用的属性，包括抵抗后处理和对手使用边信息。虽然差分隐私是首次提出的静态数据库应用程序，这篇评论的重点是它在控制系统的上下文中的使用，其中处理的数据往往采取数据流的形式。通过两个主要的应用程序过滤和优化算法，我们说明了使用控制和优化的数学工具，将非私有算法转换为私有对应。这些工具还使我们能够量化隐私和系统性能之间的权衡。
Many modern dynamical systems, such as smart grids and traffic networks, rely on user data for efficient operation. These data often contain sensitive information that the participating users do not wish to reveal to the public. One major challenge is to protect the privacy of participating users when utilizing user data. Over the past decade, differential privacy has emerged as a mathematically rigorous approach that provides strong privacy guarantees. In particular, differential privacy has several useful properties, including resistance to both postprocessing and the use of side information by adversaries. Although differential privacy was first proposed for static-database applications, this review focuses on its use in the context of control systems, in which the data under processing often take the form of data streams. Through two major applications—filtering and optimization algorithms—we illustrate the use of mathematical tools from control and optimization to convert a nonprivate algorithm to its private counterpart. These tools also enable us to quantify the trade-offs between privacy and system performance.