Location Privacy in Pervasive Computing
IEEE Pervasive Computing
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
SEA-CNN: Scalable Processing of Continuous K-Nearest Neighbor Queries in Spatio-temporal Databases
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Location Privacy in Mobile Systems: A Personalized Anonymization Model
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
Protecting Location Privacy Through Path Confusion
SECURECOMM '05 Proceedings of the First International Conference on Security and Privacy for Emerging Areas in Communications Networks
The new Casper: query processing for location services without compromising privacy
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
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
PRIVE: anonymous location-based queries in distributed mobile systems
Proceedings of the 16th international conference on World Wide Web
An Omnipresent Formal Trust Model (FTM) for Pervasive Computing Environment
COMPSAC '07 Proceedings of the 31st Annual International Computer Software and Applications Conference - Volume 01
Preventing Location-Based Identity Inference in Anonymous Spatial Queries
IEEE Transactions on Knowledge and Data Engineering
Supporting anonymous location queries in mobile environments with privacygrid
Proceedings of the 17th international conference on World Wide Web
Private queries in location based services: anonymizers are not necessary
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Toward a distributed k-anonymity protocol for location privacy
Proceedings of the 7th ACM workshop on Privacy in the electronic society
Location privacy based on trusted computing and secure logging
Proceedings of the 4th international conference on Security and privacy in communication netowrks
Anonymity in Location-Based Services: Towards a General Framework
MDM '07 Proceedings of the 2007 International Conference on Mobile Data Management
Inference attacks on location tracks
PERVASIVE'07 Proceedings of the 5th international conference on Pervasive computing
Safeguarding location privacy in wireless ad-hoc networks
UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
Location-based crowdsourcing of hyperlocal news: dimensions of participation preferences
Proceedings of the 17th ACM international conference on Supporting group work
A classification of location privacy attacks and approaches
Personal and Ubiquitous Computing
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Despite increasing popularity, Location-based Services (LBS) (e.g., searching nearby points-of-interest on map) on mobile handheld devices have been subject to major privacy concerns for users. The existing third-party privacy protection methods hide the exact location of users from service providers by sending cloaking regions (CR) that contain several other user locations in the vicinity. However, this has not ensured LBS full immunity from the privacy concerns. In this paper, we describe a serious privacy problem of LBS called multi-query attack. In this attack, the exact location of the service requester can be inferred by the adversary through obtaining cloaking regions that are shrunk or extended in subsequent queries. This problem can be addressed by judiciously retaining, over a period of time, the cloaking regions for the same set of users. Most methods in the literature are weakened for considering only a static snapshot of users during evaluation. Thus, any update due to user movements in real time becomes very costly. Our proposed approach, ANNC (Adaptive Nearest Neighborhood Cloaking) ,emphasizes developing disjoint sets of users dynamically over time in order to share the common CRs. The CRs are organized in balanced binary trees with restricted height. Thus ANNC achieves the balance between search efficiency and quality of cloaking with higher anonymity levels. The experimental evaluation demonstrates that ANNC will be more efficient in practice than other well-known approaches.