Privacy of Groups in Dense Street Imagery

TL;DR

This paper reveals that dense street imagery datasets, despite anonymization efforts, can still expose sensitive group information through AI analysis, raising significant privacy concerns and suggesting the need for better protections.

Contribution

It demonstrates how AI can infer group memberships from anonymized street images and provides a typology and privacy analysis for DSI data.

Findings

AI can infer group memberships from anonymized images

Privacy risks increase with data density and AI advancements

Recommendations for improving privacy protections in DSI

Abstract

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.