TY - GEN
T1 - Show me how you move and i will tell you who you are
AU - Gambs, Sébastien
AU - Killijian, Marc Olivier
AU - Del Prado Cortez, Miguel Núñez
PY - 2010/12/1
Y1 - 2010/12/1
N2 - Due to the emergence of geolocated applications, more and more mobility traces are generated on a daily basis and collected in the form of geolocated datasets. If an unauthorized entity can access this data, it can used it to infer personal information about the individuals whose movements are contained within these datasets, such as learning their home and place of work or even their social network, thus causing a privacy breach. In order to protect the privacy of individuals, a sanitization process, which adds uncertainty to the data and removes some sensible information, has to be performed. The global objective of GEPETO (for GEoPrivacy Enhancing TOolkit) is to provide researchers concerned with geo-privacy with means to evaluate various sanitization techniques and inference attacks on geolocated data. In this paper, we report on our preliminary experiments with GEPETO for comparing different clustering algorithms and heuristics that can be used as inference attacks, and evaluate their efficiency for the identification of point of interests, as well as their resilience to sanitization mechanisms such as sampling and perturbation. Copyright 2010 ACM.
AB - Due to the emergence of geolocated applications, more and more mobility traces are generated on a daily basis and collected in the form of geolocated datasets. If an unauthorized entity can access this data, it can used it to infer personal information about the individuals whose movements are contained within these datasets, such as learning their home and place of work or even their social network, thus causing a privacy breach. In order to protect the privacy of individuals, a sanitization process, which adds uncertainty to the data and removes some sensible information, has to be performed. The global objective of GEPETO (for GEoPrivacy Enhancing TOolkit) is to provide researchers concerned with geo-privacy with means to evaluate various sanitization techniques and inference attacks on geolocated data. In this paper, we report on our preliminary experiments with GEPETO for comparing different clustering algorithms and heuristics that can be used as inference attacks, and evaluate their efficiency for the identification of point of interests, as well as their resilience to sanitization mechanisms such as sampling and perturbation. Copyright 2010 ACM.
KW - Clustering
KW - Geo-privacy
KW - Geolocated data
KW - Inference attacks
KW - Privacy
KW - Sanitization
KW - Clustering
KW - Geo-privacy
KW - Geolocated data
KW - Inference attacks
KW - Privacy
KW - Sanitization
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=78650879093&origin=inward
UR - https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=78650879093&origin=inward
U2 - 10.1145/1868470.1868479
DO - 10.1145/1868470.1868479
M3 - Conference contribution
SP - 34
EP - 41
BT - SPRINGL '10: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS
T2 - Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS, SPRINGL 2010
Y2 - 1 December 2010
ER -