On Privacy of Dynamical Systems: An Optimal Probabilistic Mapping Approach

• Published in: IEEE transactions on information forensics and security Vol. 16; pp. 2608 - 2620
• Main Authors: Murguia, Carlos, Shames, Iman, Farokhi, Farhad, Nesic, Dragan, Poor, H. Vincent
• Format: Journal Article
• Language: English
• Published: New York IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Additives; Constraint modelling; Data privacy; Distortion measurement; Dynamical systems; Linear functions; Measurement; Mutual information; Optimization; Privacy; Probability distribution; quantization; Remote sensors; Sensors; Statistical analysis; Windows (intervals)
• ISSN: 1556-6013; 1556-6021
• DOI: 10.1109/TIFS.2021.3055022

We address the problem of maximizing privacy of stochastic dynamical systems whose state information is released through quantized sensor data. In particular, we consider the setting where information about the system state is obtained using noisy sensor measurements. This data is quantized and transmitted to a (possibly untrustworthy) remote station through a public/unsecured communication network. We aim at keeping (part of) the state of the system private; however, because the network (and/or the remote station) might be unsecure, adversaries might have access to sensor data, which can be used to estimate the system state. To prevent such adversaries from obtaining an accurate state estimate, before transmission, we randomize quantized sensor data using additive random vectors, and send the corrupted data to the remote station instead. We design the joint probability distribution of these additive vectors (over a time window) to minimize the mutual information (our privacy metric) between some linear function of the system state (a desired private output) and the randomized sensor data for a desired level of distortion -how different quantized sensor measurements and distorted data are allowed to be. We pose the problem of synthesising the joint probability distribution of the additive vectors as a convex program subject to linear constraints. Simulation experiments are presented to illustrate our privacy scheme.