Privacy-Preserving State Estimation with Crowd Sensors: An Information-Theoretic Respective
For system designers using crowd-sourced sensor networks, this work provides a theoretical framework to guarantee privacy against an omnipotent adversary while maintaining state estimation utility.
This paper addresses privacy-preserving state estimation in linear time-invariant dynamical systems using crowd sensors, where a Luenberger-like observer fuses measurements from randomly selected sensors. It shows that any prescribed level of information leakage, measured via mutual information, is achievable by tuning the variance of additive privacy-preserving noise, enabling fine-tuned privacy-utility trade-offs.
Privacy-preserving state estimation for linear time-invariant dynamical systems with crowd sensors is considered. At any time step, the estimator has access to measurements from a randomly selected sensor from a pool of sensors with pre-specified models and noise profiles. A Luenberger-like observer is used to fuse the measurements with the underlying model of the system to recursively generate the state estimates. An additive privacy-preserving noise is used to constrain information leakage. Information leakage is measured via mutual information between the identity of the sensors and the state estimate conditioned on the actual state of the system. This captures an omnipotent adversary that not only can access state estimates but can also gather direct high-quality state measurements. Any prescribed level of information leakage is shown to be achievable by appropriately selecting the variance of the privacy-preserving noise. Therefore, privacy-utility trade-off can be fine-tuned.