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2018 NIPS NeurIPS 2018

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 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.

🌉 Interdisciplinary Bridge — Data Science & Analytics and Machine Learning
🧭 Keyword Pioneer — privacy-preserving analytics
🐣 Hot Topic Early Bird — time series analysis
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Rachel Cummings , Sara Krehbiel , Yajun Mei , Rui Tuo , Wanrong Zhang

Topics

Machine Learning > Optimization & Theory > Statistical Learning Machine Learning > Application Areas > Privacy Data Science & Analytics > Methods > Time Series Data Science & Analytics > Methods > Time Series Analysis Machine Learning > Learning Types > Privacy

Keywords

time series analysis statistical learning differential privacy change-point detection sequential datum online detection offline detection privacy-preserving analytics
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