Differentially Private Change-Point Detection

By Rachel Cummings, Sara Krehbiel, Yajun Mei, Rui Tuo, Wanrong Zhang. 2018

35 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Science Applications, Anomaly & Outlier Detection, Privacy & Security

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Science Applications

In Privacy & Security

Citations in the corpus

The structure of optimal private tests for simple hypotheses (2018, background): “[22] recently gave the first private algorithms for change-point detection.”