Differentially Private Change-Point Detection
Rachel Cummings, Sara Krehbiel, Yajun Mei, Rui Tuo, Wanrong Zhang
Abstract
The change-point detection problem seeks to identify distributional changes at an unknown change-point k* in a stream of data. This problem appears in many important practical settings involving personal data, including biosurveillance, fault detection, finance, signal detection, and security systems. The field of differential privacy offers data analysis tools that provide powerful worst-case privacy guarantees. We study the statistical problem of change-point problem through the lens of differential privacy. We give private algorithms for both online and offline change-point detection, analyze these algorithms theoretically, and then provide empirical validation of these results.
BibTeX
@inproceedings{NEURIPS2018_f19ec2b8,
author = {Cummings, Rachel and Krehbiel, Sara and Mei, Yajun and Tuo, Rui and Zhang, Wanrong},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Differentially Private Change-Point Detection},
url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/f19ec2b84181033bf4753a5a51d5d608-Paper.pdf},
volume = {31},
year = {2018}
}