TY - GEN
T1 - Protecting location privacy against inference attacks
AU - Minami, Kazuhiro
AU - Borisov, Nikita
PY - 2010
Y1 - 2010
N2 - GPS-enabled mobile devices are a quickly growing market and users are starting to share their location information with each other through services such as Google Latitude. Location information, however, is very privacy-sensitive, since it can be used to infer activities, preferences, relationships, and other personal information, and thus access to it must be carefully protected. The situation is complicated by the possibility of inferring a users' location information from previous (or even future) movements. We argue that such inference means that traditional access control models that make a binary decision on whether a piece of information is released or not are not sufficient, and new policies must be designed that ensure that private information is not revealed either directly or through inference. We provide a formal definition of location privacy that incorporates an adversary's ability to predict location and discuss possible implementation of access control mechanisms that satisfy this definition. To support our reasoning, we analyze a preliminary data set to evaluate the accuracy of location prediction.
AB - GPS-enabled mobile devices are a quickly growing market and users are starting to share their location information with each other through services such as Google Latitude. Location information, however, is very privacy-sensitive, since it can be used to infer activities, preferences, relationships, and other personal information, and thus access to it must be carefully protected. The situation is complicated by the possibility of inferring a users' location information from previous (or even future) movements. We argue that such inference means that traditional access control models that make a binary decision on whether a piece of information is released or not are not sufficient, and new policies must be designed that ensure that private information is not revealed either directly or through inference. We provide a formal definition of location privacy that incorporates an adversary's ability to predict location and discuss possible implementation of access control mechanisms that satisfy this definition. To support our reasoning, we analyze a preliminary data set to evaluate the accuracy of location prediction.
KW - access control
KW - location privacy
KW - the markov model
UR - http://www.scopus.com/inward/record.url?scp=78650189312&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78650189312&partnerID=8YFLogxK
U2 - 10.1145/1866919.1866938
DO - 10.1145/1866919.1866938
M3 - Conference contribution
AN - SCOPUS:78650189312
SN - 9781450300964
T3 - Proceedings of the ACM Conference on Computer and Communications Security
SP - 123
EP - 126
BT - Proceedings of the 9th Annual ACM Workshop on Privacy in the Electronic Society, WPES '10, Co-located with CCS'10
T2 - 9th Annual ACM Workshop on Privacy in the Electronic Society, WPES '10, Co-located with CCS'10
Y2 - 4 October 2010 through 8 October 2010
ER -