Location Privacy through Secret Sharing Techniques
WOWMOM '05 Proceedings of the First International IEEE WoWMoM Workshop on Trust, Security and Privacy for Ubiquitous Computing - Volume 03
Preventing Location-Based Identity Inference in Anonymous Spatial Queries
IEEE Transactions on Knowledge and Data Engineering
Landscape-aware location-privacy protection in location-based services
Journal of Systems Architecture: the EUROMICRO Journal
Privacy in Location-Based Services: State-of-the-Art and Research Directions
MDM '07 Proceedings of the 2007 International Conference on Mobile Data Management
A survey of computational location privacy
Personal and Ubiquitous Computing
Privacy in Georeferenced Context-Aware Services: A Survey
Privacy in Location-Based Applications
Protecting location privacy against spatial inferences: the PROBE approach
Proceedings of the 2nd SIGSPATIAL ACM GIS 2009 International Workshop on Security and Privacy in GIS and LBS
Location privacy protection through obfuscation-based techniques
Proceedings of the 21st annual IFIP WG 11.3 working conference on Data and applications security
Position sharing for location privacy in non-trusted systems
PERCOM '11 Proceedings of the 2011 IEEE International Conference on Pervasive Computing and Communications
PShare: Ensuring location privacy in non-trusted systems through multi-secret sharing
Pervasive and Mobile Computing
A classification of location privacy attacks and approaches
Personal and Ubiquitous Computing
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Many current location-based applications (LBA) such as friend finder services use information about the positions of mobile users. So-called location services (LSs) have been proposed to manage these mobile user positions efficiently. However, managing user positions raises privacy issues, in particular, if the providers of LSs are only partially trusted. Therefore, we presented the concept of private position sharing for partially trusted systems in a previous paper [1]. The basic idea of position sharing is to split the precise user position into a set of position shares of well-defined limited precision and distribute these shares among LSs of different providers. The main contributions of this paper are two extended position sharing approaches that improve our previous approach in two ways: Firstly, we reduce the predictability of share generation that allows an attacker to gain further information from a sub-set of shares to further increase the position precision. Secondly, we present a position sharing algorithm for constrained movement scenarios whereas the existing approach was tailored to open space environments. However, open space approaches are vulnerable to map-based attacks. Therefore, we present a share generation algorithm that takes map knowledge into account.