Differentially Private Kalman Filtering
This addresses privacy concerns in applications like smart grids and intelligent transportation systems, where continuous data sharing is needed for monitoring, but it is incremental as it adapts existing differential privacy concepts to a specific filtering context.
The paper tackles the problem of performing Kalman filtering while ensuring the privacy of individual participants' data, proposing a method that extends differential privacy to dynamic systems and mitigates performance loss by controlling the Hinfinity norm of the filter.
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 strong 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 Hinfinity norm of the filter. Finally, we discuss an application to a privacy-preserving traffic monitoring system.