Name
Abstract | ||
|---|---|---|
In mobile Internet, popular Location-Based Services (LBSs) recommend Point-of-Interest (POI) data according to physical positions of smartphone users. However, untrusted LBS providers can violate location privacy by analyzing user requests semantically. Therefore, this paper aims at protecting user privacy in location-based applications by evaluating disclosure risks on sensitive location semantics. First, we introduce a novel method to model location semantics for user privacy using Bayesian inference and demonstrate details of computing the semantic privacy metric. Next, we design a cloaking region construction algorithm against the leakage of sensitive location semantics. Finally, a series of experiments evaluate this solution's performance to show its availability. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-21042-1_24 | WEB-AGE INFORMATION MANAGEMENT (WAIM 2015) |
Keywords | Field | DocType |
Location privacy protection, Location semantics, Bayesian inference, Spatial cloaking | Data mining,Mobile internet,Cloaking,Frequentist inference,Bayesian inference,Computer science,Artificial intelligence,Bayesian statistics,Machine learning,User privacy,Semantics | Conference |
Volume | ISSN | Citations |
9098 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 14 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhengang Wu | 1 | 1 | 3.06 |
| Zhong Chen | 2 | 503 | 58.35 |
| Jiawei Zhu | 3 | 0 | 0.68 |
| Huiping Sun | 4 | 40 | 8.68 |
| Zhi Guan | 5 | 24 | 4.23 |