  Massive amounts of video data are ubiquitously generated in personal devices
and dedicated video recording facilities. Analyzing such data would be
extremely beneficial in real world (e.g., urban traffic analysis, pedestrian
behavior analysis, video surveillance). However, videos contain considerable
sensitive information, such as human faces, identities and activities. Most of
the existing video sanitization techniques simply obfuscate the video by
detecting and blurring the region of interests (e.g., faces, vehicle plates,
locations and timestamps) without quantifying and bounding the privacy leakage
in the sanitization. In this paper, to the best of our knowledge, we propose
the first differentially private video analytics platform (VideoDP) which
flexibly supports different video analyses with rigorous privacy guarantee.
Different from traditional noise-injection based differentially private
mechanisms, given the input video, VideoDP randomly generates a utility-driven
private video in which adding or removing any sensitive visual element (e.g.,
human, object) does not significantly affect the output video. Then, different
video analyses requested by untrusted video analysts can be flexibly performed
over the utility-driven video while ensuring differential privacy. Finally, we
conduct experiments on real videos, and the experimental results demonstrate
that our VideoDP effectively functions video analytics with good utility.
