Unique in the Crowd: The Privacy Bounds of Human Mobility

Unique in the Crowd: The Privacy Bounds of Human Mobility

Yves-Alexandre de Montjoye, César A. Hidalgo, Michel Verleysen & Vincent D. Blondel. 2013. (View Paper → )

We study fifteen months of human mobility data for one and a half million individuals and find that human mobility traces are highly unique. In fact, in a dataset where the location of an individual is specified hourly and with a spatial resolution equal to that given by the carrier's antennas, four spatio-temporal points are enough to uniquely identify 95% of the individuals. We coarsen the data spatially and temporally to find a formula for the uniqueness of human mobility traces given their resolution and the available outside information. This formula shows that the uniqueness of mobility traces decays approximately as the 1/10 power of their resolution. Hence, even coarse datasets provide little anonymity. These findings represent fundamental constraints to an individual's privacy and have important implications for the design of frameworks and institutions dedicated to protect the privacy of individuals.

There are significant privacy risks posed by mobility data. Our movement patterns are highly unique. This research challenges the common assumption that anonymisation methods such as removing personal identifiers can sufficiently protect privacy. Even with reduced spatial or temporal resolution, re-identification remains likely due to the uniqueness of mobility patterns.

This paper underscores the challenges of protecting user privacy in services that collect or use location-based data. Product managers need to carefully consider privacy safeguards in the design of location-based features and ensure that robust mechanisms for data protection are in place.