Name
Abstract | ||
|---|---|---|
Privacy preservation in location-based proximity services has recently received considerable attention in geo-social networks. Nearby friends notification and social discoveries are two important types of location-based proximity services. A large number of privacy protection methods have been proposed on nearby friends notification, but few on social discoveries. Most of existing protection methods on nearby friends notification cannot be applied in social discoveries, since a secret key needs to be shared between dynamic friends. In this paper, we address the research challenges that the location privacy protection in distance-based social discoveries. We propose a novel framework DistSD for the distance-based social discovery with personalized posterior screening. We also show that the problem that finding an optimal safe group of nearby seeking users is NP-hard. Two heuristic privacy enhanced social discovery algorithms are proposed, which protect users' locations from a service result perspective. Experiments are conducted based on the real-life data and experimental results validate the effectiveness and efficiency of the proposed algorithms. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | Social discovery,Nearby friends notification,Distance-based social discovery |
Field | DocType | Citations |
Data mining,Heuristic,World Wide Web,Computer science,Cryptography,Quality of service,Big data,Mobile telephony | Conference | 0 |
PageRank | References | Authors |
0.34 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiao Pan | 1 | 8 | 1.47 |
| Jiawei Zhang | 2 | 806 | 72.17 |
| Fengjiao Wang | 3 | 33 | 3.33 |
| Philip S. Yu | 4 | 30670 | 3474.16 |