Differentially private Kalman filtering

By Jerome Le Ny, George Pappas. 2012

46 citations recorded by the source corpus.

This paper studies the H2 (Kalman) filtering problem in the situation where a signal estimate must be constructed based on inputs from individual participants, whose data must remain private. This problem arises in emerging applications such as smart grids or intelligent transportation systems, where users continuously send data to third-party aggregators performing global monitoring or control tasks, and require guarantees that this data cannot be used to infer additional personal information. To provide formal privacy guarantees against adversaries with arbitrary side information, we rely on the notion of differential privacy introduced relatively recently in the database literature. This notion is extended to dynamic systems with many participants contributing independent input signals, and mechanisms are then proposed to solve the H2 filtering problem with a differential privacy constraint. A method for mitigating the impact of the privacy-inducing mechanism on the estimation performance is described, which relies on controlling the H∞ norm of the filter. Finally, we discuss an application to a privacy-preserving traffic monitoring system.

Subfields: Artificial Intelligence

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Citations in the corpus

Differential Privacy: An Economic Method for Choosing Epsilon (2014, background): “1—1 Frequent Items Ny-Pappas [38] ln3 Kalman Filtering Chaudhuri et al.”
Differentially Private Filtering (2012, background): “Preliminary versions of this paper appeared in [1] and [2].”
Differentially private iterative synchronous consensus (2012, methodology): “In the followup work [19], a Kalman filter is designed to estimate the states of differentially private systems with minimized error.”