# Privacy of Groups in Dense Street Imagery

arXiv: https://arxiv.org/abs/2505.07085 · HTML: https://mlsanity.com/paper/2505.07085/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 112 references that are in the map.
- First posted on arXiv 11 May 2025.

## Builds on (most cited first, 16 of 16)

- [Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs](https://mlsanity.com/paper/2406.16860/) (2024)
- [Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art](https://mlsanity.com/paper/1704.05519/) (2017)
- [Knock Knock, Who's There? Membership Inference on Aggregate Location Data](https://mlsanity.com/paper/1708.06145/) (2017)
- [Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US](https://mlsanity.com/paper/1702.06683/) (2017)
- [Garbage In, Garbage Out? Do Machine Learning Application Papers in Social Computing Report Where Human-Labeled Training Data Comes From?](https://mlsanity.com/paper/1912.08320/) (2019)
- [Fashion Meets Computer Vision: A Survey](https://mlsanity.com/paper/2003.13988/) (2020)
- [Privacy Protection in Street-View Panoramas using Depth and Multi-View Imagery](https://mlsanity.com/paper/1903.11532/) (2019)
- [Detecting disparities in police deployments using dashcam data](https://mlsanity.com/paper/2305.15210/) (2023)
- [To use or not to use proprietary street view images in (health and place) research? That is the question](https://mlsanity.com/paper/2402.11504/) (2024)
- [The Profiling Potential of Computer Vision and the Challenge of Computational Empiricism](https://mlsanity.com/paper/1904.10016/) (2019)
- [Trucks Don't Mean Trump: Diagnosing Human Error in Image Analysis](https://mlsanity.com/paper/2205.07333/) (2022)
- [Visions of a Discipline: Analyzing Introductory AI Courses on YouTube](https://mlsanity.com/paper/2407.13077/) (2024)
- [Fingerprinting New York City's Scaffolding Problem with Longitudinal Dashcam Data](https://mlsanity.com/paper/2402.06801/) (2024)
- [Bayesian Modeling of Zero-Shot Classifications for Urban Flood Detection](https://mlsanity.com/paper/2503.14754/) (2025)
- [The Robotability Score: Enabling Harmonious Robot Navigation on Urban Streets](https://mlsanity.com/paper/2504.11163/) (2025)
- [My Precious Crash Data: Barriers and Opportunities in Encouraging Autonomous Driving Companies to Share Safety-Critical Data](https://mlsanity.com/paper/2504.17792/) (2025)

## Built on by (most cited first, 1 of 1)

- [Towards Context-Aware Image Anonymization with Multi-Agent Reasoning](https://mlsanity.com/paper/2603.27817/) (2026)

## Abstract (arXiv)

Spatially and temporally dense street imagery (DSI) datasets have grown unbounded. In 2024, individual companies possessed around 3 trillion unique images of public streets. DSI data streams are only set to grow as companies like Lyft and Waymo use DSI to train autonomous vehicle algorithms and analyze collisions. Academic researchers leverage DSI to explore novel approaches to urban analysis. Despite good-faith efforts by DSI providers to protect individual privacy through blurring faces and license plates, these measures fail to address broader privacy concerns. In this work, we find that increased data density and advancements in artificial intelligence enable harmful group membership inferences from supposedly anonymized data. We perform a penetration test to demonstrate how easily sensitive group affiliations can be inferred from obfuscated pedestrians in 25,232,608 dashcam images taken in New York City. We develop a typology of identifiable groups within DSI and analyze privacy implications through the lens of contextual integrity. Finally, we discuss actionable recommendations for researchers working with data from DSI providers.