| Title | Privacy-Aware Quickest Change Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Lau, T. S., Tay, W. Peng |
| Conference Name | ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
| Date Published | may |
| Keywords | compositionality, control charts, data privacy, Data Sanitization, generalized likelihood ratio CuSum, GLR CuSum, GLRT statistic, Human Behavior, information privacy requirements, Lorden's minimax formulation, Maximal Leakage, minimax techniques, Optimal Stopping Time, optimization problem, privacy, privacy considerations, privacy constraint, privacy-aware quickest change detection problem, properly designed sanitization channel, pubcrawl, QCD problem, Quickest change detection, quickest detection, resilience, Resiliency |
| 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. |
| DOI | 10.1109/ICASSP40776.2020.9054446 |
| Citation Key | lau_privacy-aware_2020 |
Privacy-Aware Quickest Change Detection