Privid: Practical, Privacy-Preserving Video Analytics Queries


Frank Cangialosi, MIT CSAIL; Neil Agarwal, Princeton University; Venkat Arun, MIT CSAIL; Junchen Jiang, University of Chicago; Srinivas Narayana and Anand Sarwate, Rutgers University; Ravi Netravali, Princeton University


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, (ρ,K,ε)-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 increases error by 1-21% relative to a non-private system.

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@inproceedings {278384,
author = {Frank Cangialosi and Neil Agarwal and Venkat Arun and Srinivas Narayana and Anand Sarwate and Ravi Netravali},
title = {Privid: Practical, {Privacy-Preserving} Video Analytics Queries},
booktitle = {19th USENIX Symposium on Networked Systems Design and Implementation (NSDI 22)},
year = {2022},
isbn = {978-1-939133-27-4},
address = {Renton, WA},
pages = {209--228},
url = {},
publisher = {USENIX Association},
month = apr,

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