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.
- Census Tract
- Census Block
- Location Privacy
- Location Pair
- Work Location
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, access via your institution.
Tax calculation will be finalised at checkout
Purchases are for personal use onlyLearn about institutional subscriptions
Unable to display preview. Download preview PDF.
Andersson, F., Freedman, M., Roemer, M., Vilhuber, L.: LEHD OnTheMap Technical documentation (February 21, 2008)
Beresford, A.R., Stajano, F.: Location privacy in pervasive computing. IEEE Pervasive Computing 2(1), 46–55 (2003)
Duckham, M., Kulik, L.: A formal model of obfuscation and negotiation for location privacy. In: Gellersen, H.-W., Want, R., Schmidt, A. (eds.) Pervasive 2005. LNCS, vol. 3468, pp. 152–170. Springer, Heidelberg (2005)
Hoh, B., Gruteser, M., Xiong, H., Alrabady, A.: Preserving Privacy in GPS Traces via Density-Aware Path Cloaking. In: Proc. of ACM Conference on Computer and Communications Security (CCS) (2007)
Krumm, J.: Inference Attacks on Location Tracks. In: LaMarca, A., Langheinrich, M., Truong, K.N. (eds.) Pervasive 2007. LNCS, vol. 4480, pp. 127–143. Springer, Heidelberg (2007)
Schilit, B., Hong, J., Gruteser, M.: Wireless Location Privacy Protection. Computer 36(12), 135–137 (2003)
Sweeney, L.: Uniqueness of Simple Demographics in the U.S. Population. Laboratory for International Data Privacy, Carnegie Mellon University (2000)
Sweeney, L.: K-anonymity: a Model for Protecting Privacy. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems 10(5), 557–570 (2002)
U.S. Census Bureau. Longitudinal Employer-Household Dynamics, http://lehd.did.census.gov/led/
VirtualRDC OnTheMap Data, http://www.vrdc.cornell.edu/onthemap
Editors and Affiliations
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Golle, P., Partridge, K. (2009). On the Anonymity of Home/Work Location Pairs. In: Tokuda, H., Beigl, M., Friday, A., Brush, A.J.B., Tobe, Y. (eds) Pervasive Computing. Pervasive 2009. Lecture Notes in Computer Science, vol 5538. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01516-8_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01515-1
Online ISBN: 978-3-642-01516-8