Location Privacy in Pervasive Computing
IEEE Pervasive Computing
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Learning Significant Locations and Predicting User Movement with GPS
ISWC '02 Proceedings of the 6th IEEE International Symposium on Wearable Computers
Extracting places from traces of locations
Proceedings of the 2nd ACM international workshop on Wireless mobile applications and services on WLAN hotspots
Discovering personal gazetteers: an interactive clustering approach
Proceedings of the 12th annual ACM international workshop on Geographic information systems
Keeping ubiquitous computing to yourself: a practical model for user control of privacy
International Journal of Human-Computer Studies - Special isssue: HCI research in privacy and security is critical now
Enhancing Security and Privacy in Traffic-Monitoring Systems
IEEE Pervasive Computing
Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking
Proceedings of the 1st international conference on Mobile systems, applications and services
A model for enriching trajectories with semantic geographical information
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
A conceptual view on trajectories
Data & Knowledge Engineering
Mobility, Data Mining and Privacy: Geographic Knowledge Discovery
Mobility, Data Mining and Privacy: Geographic Knowledge Discovery
Privacy Preservation in the Publication of Trajectories
MDM '08 Proceedings of the The Ninth International Conference on Mobile Data Management
Towards trajectory anonymization: a generalization-based approach
SPRINGL '08 Proceedings of the SIGSPATIAL ACM GIS 2008 International Workshop on Security and Privacy in GIS and LBS
Never Walk Alone: Uncertainty for Anonymity in Moving Objects Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Protecting Moving Trajectories with Dummies
MDM '07 Proceedings of the 2007 International Conference on Mobile Data Management
On the Anonymity of Home/Work Location Pairs
Pervasive '09 Proceedings of the 7th International Conference on Pervasive Computing
A survey of computational location privacy
Personal and Ubiquitous Computing
Location-based activity recognition using relational Markov networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Inference attacks on location tracks
PERVASIVE'07 Proceedings of the 5th international conference on Pervasive computing
GEPETO: A GEoPrivacy-Enhancing TOolkit
WAINA '10 Proceedings of the 2010 IEEE 24th International Conference on Advanced Information Networking and Applications Workshops
SeMiTri: a framework for semantic annotation of heterogeneous trajectories
Proceedings of the 14th International Conference on Extending Database Technology
Predestination: inferring destinations from partial trajectories
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
A comparative privacy analysis of geosocial networks
Proceedings of the 4th ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS
Next place prediction using mobility Markov chains
Proceedings of the First Workshop on Measurement, Privacy, and Mobility
On the complexity of aggregating information for authentication and profiling
DPM'11 Proceedings of the 6th international conference, and 4th international conference on Data Privacy Management and Autonomous Spontaneus Security
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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 use 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 sensitive 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. We describe our experiments conducted with GEPETO for comparing different inference attacks, and evaluating their efficiency for the identification of point of interests, as well as their resilience to sanitization mechanisms such as sampling and perturbation. We also introduce a mobility model that we coin as mobility Markov Chain, which can represent in a compact yet precise way the mobility behaviour of an individual. Finally, we describe an algorithm for learning such a structure from the mobility traces of an individual and we report on experimentations performed with real mobility data.