Differentially Private Kalman Filtering

  • Jerome Le NyEmail author
Part of the SpringerBriefs in Electrical and Computer Engineering book series (BRIEFSELECTRIC)


This chapter is concerned with the design of model-based differentially private filters, when the privacy-sensitive signal to be processed can be modeled as the output of a linear finite-dimensional system with publicly known parameters. Such models can capture for example known physical laws that govern the behavior of the input signal, e.g., a kinematic model linking position and velocity measurements obtained from individual users. In the absence of privacy constraint, Kalman filtering provides a solution to the problem of estimating the state of the system while minimizing the mean square error. We adapt here this filter to accommodate differential privacy constraints, for various scenarios involving either individual or collective signals.


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Copyright information

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Electrical EngineeringPolytechnique MontréalMontrealCanada

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