Communications of the ACM
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
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
Preventing Location-Based Identity Inference in Anonymous Spatial Queries
IEEE Transactions on Knowledge and Data Engineering
Privately querying location-based services with SybilQuery
Proceedings of the 11th international conference on Ubiquitous computing
Preventing velocity-based linkage attacks in location-aware applications
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Toward trustworthy mobile sensing
Proceedings of the Eleventh Workshop on Mobile Computing Systems & Applications
Position sharing for location privacy in non-trusted systems
PERCOM '11 Proceedings of the 2011 IEEE International Conference on Pervasive Computing and Communications
Fine-Grained Cloaking of Sensitive Positions in Location-Sharing Applications
IEEE Pervasive Computing
A formal model of obfuscation and negotiation for location privacy
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
Map-Aware position sharing for location privacy in non-trusted systems
Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
| Hi-index | 0.00 |
Location-based applications such as Facebook Places, Foursquare, or Loopt typically use location services to manage mobile object positions. However, exposing precise user positions raises user privacy concerns, especially if location service providers are not fully trusted. To enable the secure management of private user positions in non-trusted systems, we present two novel position sharing approaches based on the concept of multi-secret sharing. We improve existing geometric position sharing approaches by Durr et al. [2] and Skvortsov et al. [3] by considering continuous position updates and by increasing the robustness against various attacks. Furthermore, we present the first position sharing approach for symbolic location models.