Location Privacy in Pervasive Computing
IEEE Pervasive Computing
Preserving Privacy in Environments with Location-Based Applications
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
Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms
IEEE Transactions on Mobile Computing
Computational Geometry: Algorithms and Applications
Computational Geometry: Algorithms and Applications
Private queries in location based services: anonymizers are not necessary
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Secure kNN computation on encrypted databases
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Privacy-Aware Proximity Based Services
MDM '09 Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware
Privacy-Conscious Location-Based Queries in Mobile Environments
IEEE Transactions on Parallel and Distributed Systems
Private and Flexible Proximity Detection in Mobile Social Networks
MDM '10 Proceedings of the 2010 Eleventh International Conference on Mobile Data Management
Processing private queries over untrusted data cloud through privacy homomorphism
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
The VLDB Journal — The International Journal on Very Large Data Bases
Authenticating location-based services without compromising location privacy
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Protecting Location Privacy against Location-Dependent Attacks in Mobile Services
IEEE Transactions on Knowledge and Data Engineering
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Proximity detection is an important location-based service (LBS) that helps mobile users find nearby friends. However, this service usually requires users to submit their locations to the server, which raises privacy concerns. In this paper, we propose symmetric proximity detection on vicinity regions as a fair and privacy-preserving solution to this problem. Specifically, each user can specify a nearby area as vicinity region, and two users are considered to be in proximity only if they are located in each other's vicinity region. To enable efficient detection, we design a shift-and-compare protocol for one-shot proximity queries without revealing users' locations or vicinity regions. Furthermore, for continuous proximity monitoring, we propose an alert-area-based location update strategy with minimal update frequency, thereby saving communication cost. Experimental results demonstrate that our proposed privacy-preserving techniques achieve high efficiency in terms of both computation and communication costs.