Analytics on video recorded by cameras in public areas have the potential to fuel many exciting applications, but also pose the risk of intruding on individuals' privacy. Unfortunately, existing solutions fail to practically resolve this tension between utility and privacy, relying on perfect detection of all private information in each video frame--an elusive requirement. This paper presents: (1) a new notion of differential privacy (DP) for video analytics, $(\rho,K,\epsilon)$-event-duration privacy, which protects all private information visible for less than a particular duration, rather than relying on perfect detections of that information, and (2) a practical system called Privid that enforces duration-based privacy even with the (untrusted) analyst-provided deep neural networks that are commonplace for video analytics today. Across a variety of videos and queries, we show that Privid achieves accuracies within 79-99% of a non-private system.
翻译:公共区域摄像头所摄录的视频分析有可能刺激许多令人兴奋的应用,但也有可能造成侵犯个人隐私的风险。 不幸的是,现有的解决方案未能切实解决公用和隐私之间的紧张关系,依赖于对每个视频框架中所有私人信息进行完美检测 — — 一个难以实现的要求。 本文展示了:(1) 视频分析工具有区别隐私的新概念(DP),$(rho, K,\epsilon)$-event-dulament 隐私,它保护所有在不到特定期限的时间里可见的私人信息,而不是依赖对这些信息的完美检测,以及(2) 一个称为Privid的实用系统,它强制实施基于期限的隐私,即使与(无信托的)分析师提供的用于视频分析的深层神经网络是当今常见的。 在各种视频和询问中,我们展示了Privid在非私营系统的79-99%范围内实现理解。