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Unique in the Crowd: The privacy bounds of human mobility
webAuthors
Yves-Alexandre de Montjoye·César A. Hidalgo·Michel Verleysen·Vincent D. Blondel
A seminal Nature Scientific Reports paper relevant to AI safety discussions around data privacy, surveillance risks, and the limits of anonymization in datasets used for training or monitoring AI systems.
Paper Details
Citations
1,661
107 influential
Year
2013
Metadata
Importance: 62/100journal articleprimary source
Summary
This paper demonstrates that human mobility traces are highly unique, showing that just four spatiotemporal points are sufficient to uniquely identify 95% of individuals in a dataset of 1.5 million people. It establishes theoretical bounds on the re-identifiability of mobility data, revealing that even coarse-grained location data provides little anonymity.
Key Points
- •Only 4 spatiotemporal data points (location + time) are needed to uniquely identify 95% of individuals in mobility datasets.
- •Human mobility patterns are highly predictable and unique, making true anonymization of location data extremely difficult.
- •Even aggregated or coarsened mobility data retains enough uniqueness to allow re-identification of individuals.
- •The findings have major implications for the privacy of mobile phone records, transit data, and any location-tracking systems.
- •Provides a mathematical framework for quantifying the 'unicity' of mobility traces and the limits of anonymization techniques.
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Unique in the Crowd: The privacy bounds of human mobility | Scientific Reports
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Unique in the Crowd: The privacy bounds of human mobility
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Subjects
Applied mathematics
Applied physics
Computational science
Statistics
Abstract
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.
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Introduction
Derived from the Latin Privatus, meaning “withdraw from public life,” the notion of privacy has been foundational to the development of our diverse societies, forming the basis for individuals' rights such as free speech and religious freedom 1 . Despite its importance, privacy has mainly relied on informal protection mechanisms. For insta
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