Privacy-Aware Quickest Change Detection

Tze Siong Lau; Wee Peng Tay

ICASSP 2020accepted0 citations

Privacy-Aware Quickest Change Detection

Abstract

This paper considers the problem of the quickest detection of a change in distribution while taking privacy considerations into account. Our goal is to sanitize the signal to satisfy information privacy requirements while being able to detect a change quickly. We formulate the privacy-aware quickest change detection (QCD) problem by including a privacy constraint to Lorden's minimax formulation. We show that the Generalized Likelihood Ratio (GLR) CuSum achieves asymptotic optimality with a properly designed sanitization channel and formulate the design of this sanitization channel as an optimization problem. For computational tractability, a continuous relaxation for the discrete counting constraint is proposed and the augmented Lagrangian method is applied to obtain locally optimal solutions.

BibTeX

@inproceedings{icassp2020_privacyawarequic,
  title = {Privacy-Aware Quickest Change Detection},
  author = {Tze Siong Lau and Wee Peng Tay},
  booktitle = {ICASSP 2020},
  year = {2020}
}