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Locally private online change point detection
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Berrett, Thomas and Yu, Yi (2021) Locally private online change point detection. In: NeurIPS 2022, New Orleans, La, USA, 28 Nov - 09 Dec 2022. Published in: Advances in Neural Information Processing Systems (Submitted)
An open access version can be found in:
Official URL: https://openreview.net/forum?id=KfC0i9Hjvl2
Abstract
We study online change point detection problems under the constraint of local differential privacy (LDP) where, in particular, the statistician does not have access to the raw data. As a concrete problem, we study a multivariate nonparametric regression problem. At each time point
, the raw data are assumed to be of the form
, where
is a
-dimensional feature vector and
is a response variable. Our primary aim is to detect changes in the regression function
as soon as the change occurs. We provide algorithms which respect the LDP constraint, which control the false alarm probability, and which detect changes with a minimal (minimax rate-optimal) delay. To quantify the cost of privacy, we also present the optimal rate in the benchmark, non-private setting. These non-private results are also new to the literature and thus are interesting \emph{per se}. In addition, we study the univariate mean online change point detection problem, under privacy constraints. This serves as the blueprint of studying more complicated private change point detection problems.
Item Type: | Conference Item (Poster) | ||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||
Journal or Publication Title: | Advances in Neural Information Processing Systems | ||||||
Official Date: | 28 September 2021 | ||||||
Dates: |
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Status: | Peer Reviewed | ||||||
Publication Status: | Submitted | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
RIOXX Funder/Project Grant: |
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Conference Paper Type: | Poster | ||||||
Title of Event: | NeurIPS 2022 | ||||||
Type of Event: | Conference | ||||||
Location of Event: | New Orleans, La, USA | ||||||
Date(s) of Event: | 28 Nov - 09 Dec 2022 | ||||||
Related URLs: | |||||||
Open Access Version: |
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