  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.
