Privacy of Groups in Dense Street Imagery
Journal:
arXiv
Published Date:
May 11, 2025
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.
