Name
Abstract | ||
|---|---|---|
In mobile Internet, Location-Based Services (LBSs) as a popular kind of context-aware recommendation systems can recommend Point of Interest (POI) data according to current locations of users. However, the inherent feature leads to leak sensitive location information of users into untrusted LBS providers. This paper aims at the location privacy problem on query prediction which forecasts next locations and violates user privacy seriously. To tackle this, we propose a novel location privacy protection solution. The contribution is three-fold. First, we model query prediction on cloaking regions using the Bayesian inference. Next, the proposed location anonymization method can generalize locations into safer cloaking regions against such query prediction attacks. Finally, a series of experiments evaluate the performance of this solution and demonstrate its availability. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/UIC-ATC-ScalCom.2014.19 | UIC/ATC/ScalCom |
Keywords | Field | DocType |
Location Privacy Protection, Location Prediction, Bayesian Inference, Location Cloaking, Cloaking Regions | Recommender system,Data mining,Cloaking,Bayesian inference,Computer science,SAFER,Location-based service,Point of interest,Pound (mass),User privacy | Conference |
Citations | PageRank | References |
0 | 0.34 | 25 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhengang Wu | 1 | 1 | 3.06 |
| Liangwen Yu | 2 | 7 | 3.49 |
| Jiawei Zhu | 3 | 16 | 7.17 |
| Huiping Sun | 4 | 40 | 8.68 |
| Zhi Guan | 5 | 76 | 10.75 |
| Zhong Chen | 6 | 503 | 58.35 |