Differentially Private Change-Point Detection
This addresses privacy concerns in sensitive applications like biosurveillance and finance, offering a novel integration of differential privacy into change-point detection.
The paper tackles the problem of detecting distributional changes in data streams while ensuring differential privacy, providing both online and offline algorithms with theoretical analysis and empirical validation.
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.