Protecting privacy, in continuous location-tracking applications

By Marco Gruteser, Xuan Liu. IEEE Security & Privacy Magazine, 2004

295 citations recorded by the source corpus.

How researchers see this paper

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Querying imprecise data in moving object environments (2003, background): “By correlating L(t) with a map, it is easy to obtain the region the user is in. If the area is sensitive [ 8 ], ��”
Blind Evaluation of Nearest Neighbor Queries Using Space Transformation to Preserve Location Privacy (2007, background): “Several techniques based on cloaking and K-anonymity have been proposed in the literature to reduce the resolution of the user’s location information [1, 2, 4–6, 13, 14].”
The new Casper: query processing for location services without compromising privacy (2006, unknown): “Our employed privacy proﬁle matches the privacy requirements of mobiles users as depicted by several social studies (e.g., see [6, 18, 20, 26, 32]).”
Location privacy in pervasive computing (2008, background): “Finally, another strand of research is aimed at protecting the path privacy of the users [23] [25] [30].”
Anonymization of location data does not work: a large-scale measurement study (2011, background): “Location privacy, the risks associated with the release of location data and the techniques to quantify and mitigate those risks, have gained much attention in the literature [11, 10, 12, 14, 1, 13].”
Preserving User Location Privacy in Mobile Data Management Infrastructures (2006, background): “The notion of k-anonymity has also been used in a number of works that study location privacy [2,4,15].”