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Poster
Differentially Private Change-Point Detection
Rachel Cummings · Sara Krehbiel · Yajun Mei · Rui Tuo · Wanrong Zhang

Wed Dec 05 02:00 PM -- 04:00 PM (PST) @ Room 210 #95
in Wed Poster Session B »

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.

Keywords: Privacy, Anonymity, and Security · Adaptive Data Analysis
[ Paper] 

Author Information

Rachel Cummings (Georgia Tech)
Sara Krehbiel (University of Richmond)
Yajun Mei (Georgia Institute of Technology)
Rui Tuo (Texas A&M University)
Wanrong Zhang (Georgia Institute of Technology)

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