2019/12/08 by Niladri Das, Das, Niladri, Raktim Bhattacharya +1 · 1 citation
Computer Science · Engineering · #Distributed Sensor Networks and Detection Algorithms #Space Satellite Systems and Control #Target Tracking and Data Fusion in Sensor Networks
paper · pdf · doi:10.48550/arxiv.1912.03775
In this paper, we present an optimization-based formulation for\nprivacy-utility tradeoff in the Ensemble and Unscented Kalman filtering\nframework, with a focus on space situational awareness. Privacy and utility are\ndefined in terms of a lower and an upper bound on the state estimation error\ncovariance, respectively. Synthetic sensor noise is used to satisfy these\nbounds and is determined by solving an optimization problem. Given privacy and\nutility bounds, we present optimization problem formulations to determine a)\nthe maximum noise for which utility is satisfied or the estimation errors are\nupper-bounded, b) the minimum noise for which privacy is satisfied or the\nestimation errors are lower-bounded, c) the optimal noise that satisfies\nutility constraints and maximizes privacy, and d) the optimal noise that\nsatisfies privacy constraints and maximizes utility. We demonstrate application\nof these formulations to the tracking of the International Space Station, and\nhighlight the optimal privacy vs utility tradeoff for this dynamical system.\n