Differentially private change-point detection
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The change-point detection problem seeks to identify distributional changes in streams of data. Increasingly, tools for change-point detection are applied in settings where data may be highly sensitive and formal privacy guarantees are required, such as identifying disease outbreaks based on hospital records, or IoT devices detecting activity within a home. Differential privacy has emerged as a powerful technique for enabling data analysis while preventing information leakage about individuals. 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 when the pre-change and post-change distributions are completely known and also a more realistic setting when distributions are unknown. We analyze these algorithms theoretically, and provide empirical validation of our results.