Differentially Private Change-Point Detection

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.

Cited by in corpus (6)

• A Primer on Private Statistics
• Private Identity Testing for High-Dimensional Distributions
• Privately detecting changes in unknown distributions
• Locally private online change point detection
• A Two-Stage Architecture for Differentially Private Kalman Filtering and LQG Control
• Quickest Change Detection with Privacy Constraint