# Privid: Practical, Privacy-Preserving Video Analytics Queries

arXiv: https://arxiv.org/abs/2106.12083 · HTML: https://mlsanity.com/paper/2106.12083/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2021.

## Built on by (most cited first, 7 of 7)

- [Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey](https://mlsanity.com/paper/2309.16398/) (2023)
- [MadEye: Boosting Live Video Analytics Accuracy with Adaptive Camera Configurations](https://mlsanity.com/paper/2304.02101/) (2023)
- [Real-Time Privacy Preservation for Robot Visual Perception](https://mlsanity.com/paper/2505.05519/) (2025)
- [Enabling Cross-Camera Collaboration for Video Analytics on Distributed Smart Cameras](https://mlsanity.com/paper/2401.14132/) (2024)
- [CloudEye: A New Paradigm of Video Analysis System for Mobile Visual Scenarios](https://mlsanity.com/paper/2410.18399/) (2024)
- [RedunCut: Measurement-Driven Sampling and Accuracy Performance Modeling for Low-Cost Live Video Analytics](https://mlsanity.com/paper/2512.24386/) (2025)
- [Search over the Visual World: Persistent Visual Memory, Layered Indexes, and Source-Grounded Evidence](https://mlsanity.com/paper/2608.08075/) (2026)

## Abstract (arXiv)

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 achieves accuracies within 79-99% of a non-private system.