Privacy-Aware Proximity Based Services

Proximity based services are location based services (LBS) in which  the service adaptation depends on the comparison between a given threshold value and the distance between a user and other (possibly moving) entities.  While privacy preservation in LBS has lately received much attention, very limited work has been done on privacy-aware proximity based services.  This paper describes the  main privacy threats that the usage of these services can lead to, and proposes original privacy preservation techniques offering different trade-offs between quality of service and privacy preservation. The properties of the proposed algorithms are formally proved, and an extensive experimental work illustrates the practicality of the approach.

[1]  Sushil Jajodia,et al.  Time Granularities in Databases, Data Mining, and Temporal Reasoning , 2000, Springer Berlin Heidelberg.

[2]  Yiqi Dai,et al.  Symmetric Encryption Solutions to Millionaire's Problem and Its Extension , 2006, International Conference on Digital Information Management.

[3]  Claudio Bettini,et al.  A Comparison of Spatial Generalization Algorithms for LBS Privacy Preservation , 2007, 2007 International Conference on Mobile Data Management.

[4]  Ian Goldberg,et al.  Louis, Lester and Pierre: Three Protocols for Location Privacy , 2007, Privacy Enhancing Technologies.

[5]  Claudio Bettini,et al.  Spatial generalisation algorithms for LBS privacy preservation , 2007, J. Locat. Based Serv..

[6]  Kyriakos Mouratidis,et al.  Preventing Location-Based Identity Inference in Anonymous Spatial Queries , 2007, IEEE Transactions on Knowledge and Data Engineering.

[7]  A. Khoshgozaran,et al.  SPIRAL: A Scalable Private Information Retrieval Approach to Location Privacy , 2008, 2008 Ninth International Conference on Mobile Data Management Workshops, MDMW.

[8]  Hua Lu,et al.  SpaceTwist: Managing the Trade-Offs Among Location Privacy, Query Performance, and Query Accuracy in Mobile Services , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[9]  Ling Liu,et al.  Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms , 2008, IEEE Transactions on Mobile Computing.

[10]  Panos Kalnis,et al.  Private queries in location based services: anonymizers are not necessary , 2008, SIGMOD Conference.