Privid: Practical, Privacy-Preserving Video Analytics Queries

TL;DR

Privid introduces a practical privacy-preserving system for video analytics that protects private information based on duration, maintaining high accuracy while addressing privacy concerns.

Contribution

The paper proposes a new differential privacy notion for video analytics and develops Privid, a system that enforces duration-based privacy with untrusted neural networks.

Findings

Achieves 79-99% accuracy of non-private systems across various videos and queries.

Introduces a novel privacy notion: $( ho,K, ext{epsilon})$-event-duration privacy.

Demonstrates practical applicability of privacy-preserving video analytics.

Abstract

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.