Privid: Practical, Privacy-Preserving Video Analytics Queries
Privid: Practical, Privacy-Preserving Video Analytics Queries
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发表时间：
2021-06
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通讯作者：
Frank Cangialosi;Neil Agarwal;V. Arun;Junchen Jiang;Srinivas Narayana;Anand D. Sarwate;Ravi Netravali
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作者：
Frank Cangialosi;Neil Agarwal;V. Arun;Junchen Jiang;Srinivas Narayana;Anand D. Sarwate;Ravi Netravali
对公共区域摄像头记录的视频进行分析有可能推动许多令人兴奋的应用，但也有侵犯个人隐私的风险。不幸的是，现有的解决方案无法实际解决实用性和隐私之间的这种紧张关系，依赖于对每个视频帧中所有私人信息的完美检测-这是一个难以捉摸的要求。本文介绍：（1）用于视频分析的差分隐私（DP）的新概念，（\rho，K，\n）$-事件持续时间隐私，它保护所有在特定持续时间内可见的私人信息，而不是依赖于对该信息的完美检测，（2）一个名为Privid的实用系统，即使在（不可信）分析师提供的深度神经网络在当今的视频分析中很常见。在各种视频和查询中，我们表明Privid在非私有系统的79-99%范围内实现了准确性。
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.