2022/11/07 by Haleh Hayati, Hayati, Haleh, Carlos Murguia +3 · 2 citations
Computer Science · Decision Sciences · Engineering · Mathematics · #Advanced Bandit Algorithms Research #Adversarial Robustness in Machine Learning #Anomaly detection #Artificial intelligence #Computer science #Computer security #Data mining #Detector #Differential entropy #Differential privacy #Entropy (arrow of time) #FOS: Electrical engineering #Gaussian #Information theory #Joint entropy #Mathematical optimization #Mathematics #Principle of maximum entropy #Private information retrieval #Real-time computing #Smart Grid Security and Resilience #Systems and Control (eess.SY) #Telecommunications #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2211.03698
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2022/11/07 · openalex created_date 2022/11/13 · openalex updated_date 2026/08/05
We present a framework for designing distorting mechanisms that allow remotely operating anomaly detectors while preserving privacy. We consider the problem setting in which a remote station seeks to identify anomalies using system input-output signals transmitted over communication networks. However, disclosing true data of the system operation is not desired as it can be used to infer private information -- modeled here as a system private output. To prevent accurate estimation of private outputs by adversaries, we pass original signals through distorting (privacy-preserving) mechanisms and send the distorted data to the remote station (which inevitably leads to degraded monitoring performance). We formulate the design of these mechanisms as a privacy-utility trade-off problem. We cast the synthesis of dependent Gaussian mechanisms as the solution of a convex program where we seek to maximize privacy quantified using information-theoretic metrics (mutual information and differential entropy) over a finite window of realizations while guaranteeing a bound on monitoring performance degradation.