Privacy and Utility Aware Data Sharing for Space Situational Awareness from Ensemble and Unscented Kalman Filtering Perspective

TL;DR

This paper introduces an optimization framework to balance privacy and utility in space situational awareness data sharing, using synthetic noise in Kalman filtering to control estimation errors.

Contribution

It formulates novel optimization problems to determine optimal noise levels that satisfy privacy and utility bounds in Ensemble and Unscented Kalman filtering.

Findings

Demonstrates privacy-utility tradeoff in space object tracking

Provides optimization solutions for privacy-preserving data sharing

Shows effectiveness in tracking the International Space Station

Abstract

In this paper, we present an optimization-based formulation for privacy-utility tradeoff in the Ensemble and Unscented Kalman filtering framework, with a focus on space situational awareness. Privacy and utility are defined in terms of a lower and an upper bound on the state estimation error covariance, respectively. Synthetic sensor noise is used to satisfy these bounds and is determined by solving an optimization problem. Given privacy and utility bounds, we present optimization problem formulations to determine a) the maximum noise for which utility is satisfied or the estimation errors are upper-bounded, b) the minimum noise for which privacy is satisfied or the estimation errors are lower-bounded, c) the optimal noise that satisfies utility constraints and maximizes privacy, and d) the optimal noise that satisfies privacy constraints and maximizes utility. We demonstrate application of these formulations to the tracking of the International Space Station, and highlight the optimal privacy vs utility tradeoff for this dynamical system.