Unique in the Crowd: The privacy bounds of human mobility

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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.

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Understanding individual human mobility patterns

M. González, A L Barabási, C. Hidalgo (2008)

Despite their importance for urban planning, traffic forecasting, and the spread of biological and mobile viruses, our understanding of the basic laws governing human motion remains limited thanks to the lack of tools to monitor the time resolved location of individuals. Here we study the trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six month period. We find that in contrast with the random trajectories predicted by the prevailing Levy flight and random walk models, human trajectories show a high degree of temporal and spatial regularity, each individual being characterized by a time independent characteristic length scale and a significant probability to return to a few highly frequented locations. After correcting for differences in travel distances and the inherent anisotropy of each trajectory, the individual travel patterns collapse into a single spatial probability distribution, indicating that despite the diversity of their travel history, humans follow simple reproducible patterns. This inherent similarity in travel patterns could impact all phenomena driven by human mobility, from epidemic prevention to emergency response, urban planning and agent based modeling.

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The origin of bursts and heavy tails in human dynamics

Albert-László Barabási (2005)

The dynamics of many social, technological and economic phenomena are driven by individual human actions, turning the quantitative understanding of human behavior into a central question of modern science. Current models of human dynamics, used from risk assessment to communications, assume that human actions are randomly distributed in time and thus well approximated by Poisson processes. In contrast, there is increasing evidence that the timing of many human activities, ranging from communication to entertainment and work patterns, follow non-Poisson statistics, characterized by bursts of rapidly occurring events separated by long periods of inactivity. Here we show that the bursty nature of human behavior is a consequence of a decision based queuing process: when individuals execute tasks based on some perceived priority, the timing of the tasks will be heavy tailed, most tasks being rapidly executed, while a few experience very long waiting times. In contrast, priority blind execution is well approximated by uniform interevent statistics. These findings have important implications from resource management to service allocation in both communications and retail.

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Structure and tie strengths in mobile communication networks

, , … (2006)

Electronic databases, from phone to emails logs, currently provide detailed records of human communication patterns, offering novel avenues to map and explore the structure of social and communication networks. Here we examine the communication patterns of millions of mobile phone users, allowing us to simultaneously study the local and the global structure of a society-wide communication network. We observe a coupling between interaction strengths and the network's local structure, with the counterintuitive consequence that social networks are robust to the removal of the strong ties, but fall apart following a phase transition if the weak ties are removed. We show that this coupling significantly slows the diffusion process, resulting in dynamic trapping of information in communities, and find that when it comes to information diffusion, weak and strong ties are both simultaneously ineffective.

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Author and article information

Journal

Journal ID (nlm-ta): Sci Rep

Journal ID (iso-abbrev): Sci Rep

Title: Scientific Reports

Publisher: Nature Publishing Group

ISSN (Electronic): 2045-2322

Publication date (Electronic): 25 March 2013

Publication date Collection: 2013

Volume: 3

Electronic Location Identifier: 1376

Affiliations

[1 ]Massachusetts Institute of Technology, Media Lab , 20 Ames Street, Cambridge, MA 02139 USA

[2 ]Université catholique de Louvain, Institute for Information and Communication Technologies, Electronics and Applied Mathematics , Avenue Georges Lemaître 4, B-1348 Louvain-la-Neuve, Belgium

[3 ]Harvard University, Center for International Development , 79 JFK Street, Cambridge, MA 02138, USA

[4 ]Instituto de Sistemas Complejos de Valparaíso , Paseo 21 de Mayo, Valparaíso, Chile

[5 ]Massachusetts Institute of Technology, Laboratory for Information and Decision Systems , 77 Massachusetts Avenue, Cambridge, MA 02139, USA

Author notes

[a ] yva@ 123456mit.edu

Article

Publisher Item ID: srep01376

DOI: 10.1038/srep01376

PMC ID: 3607247

PubMed ID: 23524645

SO-VID: f27edb01-cdc7-48a7-abd2-422c5b600849

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/

Unique in the Crowd: The privacy bounds of human mobility

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Big Data Privacy

Most cited references 6

Understanding individual human mobility patterns

The origin of bursts and heavy tails in human dynamics

Structure and tie strengths in mobile communication networks

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