Unique in the Crowd: The privacy bounds of human mobility

By Y. de Montjoye, César A. Hidalgo, Michel Verleysen, Vincent D. Blondel. 2013

1,701 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Epidemiology, Transportation

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Citations in the corpus

Knock Knock, Who's There? Membership Inference on Aggregate Location Data (2017, background): “Previous location privacy research taking into account traces or profiles of single users [7, 12, 16, 24, 28, 37] does not apply to our problem, which focuses on aggregate location statistics.”
Study : Privacy Preserving Release of Spatio-temporal Density in Paris (2014, background): “Several recent studies have demonstrated the privacy risks of releasing location data by re-identifying individuals from geospatial data sets [8, 28, 13].”
Practical Differential Privacy for SQL Queries Using Elastic Sensitivity (2017, background): “Data anonymization attempts to provide privacy while allowing general-purpose analysis, but cannot be relied upon, as demonstrated by a number of re-identification attacks [13, 30, 32, 37].”
The Rise of Consumer Health Wearables: Promises and Barriers (2016, background): ““Digital traces” of behaviour such as time of activity and user location can reveal a person’s identity [46].”
The ethics of smart cities and urban science (2016, background): “Indeed, it is clear that it is possible to reverse engineer anonymization strategies by combing and combining datasets [56,57] unless the data are fully de-identified.”
What Does The Crowd Say About You? Evaluating Aggregation-based Location Privacy (2017, background): “Alas, this is ineffective, as location data is inherently unique to the user, and the identities of the subjects generating the traces can often be recov- ered [9, 18, 49].”
Predicting Personality Using Novel Mobile Phone-Based Metrics (2013, methodology): “Mobile cellular subscriptions have hit 6 billion throughout the world [1] and carriers have increasingly made available phone logs to researchers [2] as well as to their commercial partners [3].”
DeepCity: A Feature Learning Framework for Mining Location Check-Ins (2016, background): “CCS Concepts •Human-centered computing → Collaborative and social computing systems and tools; •Information systems → Data mining; Keywords Feature learning; network embedding; user profiling; locationbased social network mining.”
Unique in the shopping mall: On the reidentifiability of credit card metadata (2015, methodology): “Unicity quantifies the intrinsic reidentification risk of a data set (19).”