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

VideoDP: A Universal Platform for Video Analytics with Differential Privacy