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Annual Review of Control, Robotics, and Autonomous Systems

Volume 1, 2018

Privacy in Control and Dynamical Systems

Abstract

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.

Keyword(s): differential privacy, distributed optimization, gradient method, Kalman filter
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/content/journals/10.1146/annurev-control-060117-105018
2018-05-28
2024-05-05
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Privacy in Control and Dynamical Systems
Annual Review of Control, Robotics, and Autonomous Systems 1, 309 (2018); https://doi.org/10.1146/annurev-control-060117-105018
/content/journals/10.1146/annurev-control-060117-105018
/content/journals/10.1146/annurev-control-060117-105018
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  • Article Type: Review Article

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