alexa Unique in the Crowd: The privacy bounds of human mobility.
Bioinformatics & Systems Biology

Bioinformatics & Systems Biology

Journal of Data Mining in Genomics & Proteomics

Author(s): de Montjoye YA, Hidalgo CA, Verleysen M, Blondel VD, de Montjoye YA, Hidalgo CA, Verleysen M, Blondel VD, de Montjoye YA, Hidalgo CA, Verleysen M, Blondel VD, de Montjoye YA, Hidalgo CA, Verleysen M, Blondel VD

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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.
This article was published in Sci Rep and referenced in Journal of Data Mining in Genomics & Proteomics

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