Mining e-mail content for author identification forensics
ACM SIGMOD Record
IEEE Intelligent Systems
One-class svms for document classification
The Journal of Machine Learning Research
Enhancing Security and Privacy in Traffic-Monitoring Systems
IEEE Pervasive Computing
A peer-to-peer spatial cloaking algorithm for anonymous location-based service
GIS '06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking
Proceedings of the 1st international conference on Mobile systems, applications and services
Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms
IEEE Transactions on Mobile Computing
Private queries in location based services: anonymizers are not necessary
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
SPIRAL: A Scalable Private Information Retrieval Approach to Location Privacy
MDMW '08 Proceedings of the 2008 Ninth International Conference on Mobile Data Management Workshops
Hiding Location Information from Location-Based Services
MDM '07 Proceedings of the 2007 International Conference on Mobile Data Management
Realistic Driving Trips For Location Privacy
Pervasive '09 Proceedings of the 7th International Conference on Pervasive Computing
Privately querying location-based services with SybilQuery
Proceedings of the 11th international conference on Ubiquitous computing
Inference attacks on location tracks
PERVASIVE'07 Proceedings of the 5th international conference on Pervasive computing
MOBIHIDE: a mobilea peer-to-peer system for anonymous location-based queries
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
On the privacy of web search based on query obfuscation: a case study of TrackMeNot
PETS'10 Proceedings of the 10th international conference on Privacy enhancing technologies
On the anonymity of periodic location samples
SPC'05 Proceedings of the Second international conference on Security in Pervasive Computing
A formal model of obfuscation and negotiation for location privacy
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
Fusion of face and speech data for person identity verification
IEEE Transactions on Neural Networks
Cover locations: availing location-based services without revealing the location
Proceedings of the 10th annual ACM workshop on Privacy in the electronic society
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A promising approach to location privacy is query obfuscation, which involves reporting k -- 1 false locations along with the real location. In this paper, we examine the level of privacy protection provided by the current query obfuscation techniques against adversarial location service providers. As a representative and realistic implementation of query obfuscation, we focus on SybilQuery. We present two types of attacks depending upon whether or not a short-term query history is available. When history is available, using machine learning, we were able to identify 93.67% of user trips, with only 2.02% of fake trips misclassified, for the security parameter k = 5. In the absence of history, we used trip correlations to form a smaller set of trips effectively increasing the user query identification probability from 20% to about 40%. Our work demonstrates that the use of aggregate statistical information alone is not sufficient to generate simulated trips. We identify areas for improvement in the existing query obfuscation techniques.