@inProceedings{
 title = {On the Anonymity of Home/Work Location Pairs},
 type = {inProceedings},
 year = {2009},
 identifiers = {[object Object]},
 keywords = {anonymity,group1,privacy,summarized,ubicomp},
 pages = {390-397},
 volume = {5538},
 websites = {http://dx.doi.org/10.1007/978-3-642-01516-8_26},
 publisher = {Springer-Verlag},
 id = {36edad7f-ebae-3dd5-8c45-da54bfffa470},
 created = {2018-07-12T21:31:57.728Z},
 file_attached = {false},
 profile_id = {f954d000-ce94-3da6-bd26-b983145a920f},
 group_id = {b0b145a3-980e-3ad7-a16f-c93918c606ed},
 last_modified = {2018-07-12T21:31:57.728Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {golle:anonymity},
 source_type = {inproceedings},
 notes = {Extends the work by Krumm in finding that the disclosure of (or discovery of) both home and work locations can drastically reduce anonymity set size. For most of the US working population, they found that this knowledge reduces anonymity set size to 1, that is, to uniquely determine the person in the set... and with suitable external information, re-identifying the person. Obfuscation at the *county* level would be needed to avoid this problem.},
 private_publication = {false},
 abstract = {Many applications benefit from user location data, but location data raises privacy concerns. Anonymization can protect privacy, but identities can sometimes be inferred from supposedly anonymous data. This paper studies a new attack on the anonymity of location data. We show that if the approximate locations of an individual's home and workplace can both be deduced from a location trace, then the median size of the individual's anonymity set in the U.S. working population is 1, 21 and 34,980, for locations known at the granularity of a census block, census track and county respectively. The location data of people who live and work in different regions can be re-identified even more easily. Our results show that the threat of re-identification for location data is much greater when the individual's home and work locations can both be deduced from the data. To preserve anonymity, we offer guidance for obfuscating location traces before they are disclosed.},
 bibtype = {inProceedings},
 author = {Golle, Philippe and Partridge, Kurt},
 booktitle = {Proceedings of International Conference on Pervasive Computing}
}

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