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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 10825-10834 |
| Number of pages | 10 |
| Journal | Advances in Neural Information Processing Systems |
| Volume | 2018-December |
| State | Published - 2018 |
| Event | 32nd Conference on Neural Information Processing Systems, NeurIPS 2018 - Montreal, Canada Duration: Dec 2 2018 → Dec 8 2018 |
ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
- Signal Processing