Privacy and Utility Aware Data Sharing for Space Situational Awareness From Ensemble and Unscented Kalman Filtering Perspective
Niladri Das, Raktim Bhattacharya · IEEE Transactions on Aerospace and Electronic Systems · 2020
This article presents an optimization-based formulation for privacy-utility tradeoff in the ensemble and unscented Kalman filtering framework, focusing on the space situational awareness. Privacy and utility are defined in terms of lower and upper bound on the state estimation error covariance. The synthetic sensor noise is used to satisfy these bounds and is determined by solving an optimization problem. Given privacy and utility bounds, this article present optimization problem formulations to determine the maximum noise for which the utility is satisfied or the estimation errors are upper bounded, the minimum noise for which the privacy is satisfied or the estimation errors are lower bounded, the optimal noise that satisfies utility constraints and maximizes privacy, and the optimal noise that satisfies privacy constraints and minimizes the uncertainty. We demonstrate the application of these formulations to the tracking of the International Space Station and highlight the optimal privacy versus utility tradeoff for this dynamical system.