2021/08/03 by Haleh Hayati, Hayati, Haleh, Carlos Murguia +3
Computer Science · Engineering · #Adversarial Robustness in Machine Learning #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #FOS: Electrical engineering #Privacy-Preserving Technologies in Data #Systems and Control (eess.SY) #Wireless Communication Security Techniques #cs.CR #cs.SY #eess.SY #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2108.01755
arxiv created 2021/08/03 · openalex publication_date 2021/08/03 · arxiv updated 2021/08/05 · openalex created_date 2022/10/05 · openalex updated_date 2026/08/01
We address the problem of synthesizing distorting mechanisms that maximize privacy of stochastic dynamical systems. Information about the system state is obtained through sensor measurements. This data is transmitted to a remote station through an unsecured/public communication network. We aim to keep part of the system state private (a private output); however, because the network is unsecured, adversaries might access sensor data and input signals, which can be used to estimate private outputs. To prevent an accurate estimation, we pass sensor data and input signals through a distorting (privacy-preserving) mechanism before transmission, and send the distorted data to the trusted user. These mechanisms consist of a coordinate transformation and additive dependent Gaussian vectors. We formulate the synthesis of the distorting mechanisms as a convex program, where we minimize the mutual information (our privacy metric) between an arbitrarily large sequence of private outputs and the disclosed distorted data for desired distortion levels -- how different actual and distorted data are allowed to be.