Abstract
As Global Positioning System (GPS) receivers become a common feature in cell phones, personal digital assistants, and automobiles, there is a growing interest in tracking larger user populations, rather than individual users. Unfortunately, anonymous location samples do not fully solve the privacy problem. An adversary could link multiple samples (i.e., follow the footsteps) to accumulate path information and eventually identify a user.
This paper reports on our ongoing work to analyze privacy risks in such applications. We observe that linking anonymous location samples is related to the data association problem in tracking systems. We then propose to use such tracking algorithms to characterize the level of privacy and to derive disclosure control algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Warrior, J., McHenry, E., McGee, K.: They know where you are. IEEE Spectrum (July 2003)
Barkhuus, L., Dey, A.: Location-based services for mobile telephony: a study of users’ privacy concerns. In: 9th Internation Conference on Human-Computer Interaction, INTERACT (2003)
Sweeney, L.: k-anonymity: a model for protecting privacy. International Journal on Uncertainty. Fuzziness and Knowledge-based Systems 10(5), 557–570 (2002)
Sweeney, L.: Achieving k-Anonymity Privacy Protection Using Generalization and Suppression. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems 10(5), 571–588 (2002)
Beresford, A., Stajano, F.: Location privacy in pervasive computing. IEEE Pervasive Computing 2(1), 46–55 (2003)
Gruteser, M., Grunwald, D.: Anonymous usage of location-based services through spatial and temporal cloaking. In: Proceedings of the First International Conference on Mobile Systems, Applications, and Services (2003)
Vyas, R.: Ford device intended to unclog roads (February 2004), http://www.freep.com/money/autonews/ford27_20040227.htm
Reid, D.: An algorithm for tracking multiple targets. IEEE Transactions on Automatic Control 24(6), 843–854 (1979)
Blackman, S., Popoli, R.: Design and Analysis of Modern Tracking Systems. Artech House (1999)
Moon, T.: The expectation-maximization algorithm. IEEE Signal Processing Magazine 13(6), 47–60 (1996)
Beresford, A., Stajano, F.: Mix zones: User privacy in location-aware services. In: IEEE Workshop on Pervasive Computing and Communication Security (PerSec) (2004)
Myles, G., Friday, A., Davies, N.: Preserving privacy in environments with location-based applications. IEEE Pervasive Computing 2(1), 56–64 (2003)
Langheinrich, M.: A privacy awareness system for ubiquitous computing environments. In: 4th International Conference on Ubiquitous Computing (2002)
Duri, S., Gruteser, M., Liu, X., Moskowitz, P., Perez, R., Singh, M., Tang, J.-M.: Framework for security and privacy in automotive telematics. In: 2nd ACM International Worksphop on Mobile Commerce (2002)
Lederer, S., Mankoff, J., Dey, A.: Who wants to know what when? privacy preference determinants in ubiquitous computing. In: Extended Abstracts of Conference on Human Factors in Computing Systems (CHI), pp. 724–725 (2003)
Serjantov, A., Danezis, G.: Towards an information theoretic metric for anonymity. In: 2nd Workshop on Privacy Enhancing Technologies (2002)
Diaz, C., Seys, S., Claessens, J., Preneel, B.: Towards measuring anonymity. In: 2nd Workshop on Privacy Enhancing Technologies (2002)
Agrawal, R., Srikant, R.: Privacy-preserving data mining. In: Proc. of the ACM SIGMOD Conference on Management of Data, pp. 439–450. ACM Press, New York (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gruteser, M., Hoh, B. (2005). On the Anonymity of Periodic Location Samples. In: Hutter, D., Ullmann, M. (eds) Security in Pervasive Computing. SPC 2005. Lecture Notes in Computer Science, vol 3450. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32004-3_19
Download citation
DOI: https://doi.org/10.1007/978-3-540-32004-3_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25521-5
Online ISBN: 978-3-540-32004-3
eBook Packages: Computer ScienceComputer Science (R0)