  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. Much of the prior work on change-point
detection---including the only private algorithms for this problem---requires
complete knowledge of the pre-change and post-change distributions. However,
this assumption is not realistic for many practical applications of interest.
This work develops differentially private algorithms for solving the
change-point problem when the data distributions are unknown. Additionally, the
data may be sampled from distributions that change smoothly over time, rather
than fixed pre-change and post-change distributions. We apply our algorithms to
detect changes in the linear trends of such data streams. Finally, we also
provide experimental results to empirically validate the performance of our
algorithms.
