Differentially Private Change-Point Detection
Statistics Theory
2019-09-10 v1 Data Structures and Algorithms
Machine Learning
Statistics Theory
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 detection through the lens of differential privacy. We give private algorithms for both online and offline change-point detection, analyze these algorithms theoretically, and provide empirical validation of our results.
Keywords
Cite
@article{arxiv.1808.10056,
title = {Differentially Private Change-Point Detection},
author = {Rachel Cummings and Sara Krehbiel and Yajun Mei and Rui Tuo and Wanrong Zhang},
journal= {arXiv preprint arXiv:1808.10056},
year = {2019}
}