Name
Abstract | ||
|---|---|---|
Location based services become increasingly popular with the dramatic grows of smartphones. Personal location information is disclosed to the service providers while users are enjoying such applications. However, this data can be used to infer a user's detailed activities, or to track and predict the user's daily movements, which raises significant privacy concerns. Achieving both preserving privacy and high quality in locationbased services is still a challenge. To address this challenge, we provide a novel strategy which guarantees high service quality by ensuring the distance between a real location and the obfuscated one is bounded. For preserving location privacy, the key idea of our strategy is to select obfuscating function randomly to process data. Therefore, the real data can not be inferred by approximate or stochastic methods. This is the first paper to use such methods for preserving privacy. And the effectiveness of our strategy is demonstrated through extensive simulations. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/BIGCOM.2018.00026 | 2018 4th International Conference on Big Data Computing and Communications (BIGCOM) |
Keywords | Field | DocType |
privacy,location based services | Data mining,Service quality,Computer security,Computer science,Euclidean distance,Location-based service,Service provider,Obfuscation,Information privacy,Location awareness,Bounded function | Conference |
ISBN | Citations | PageRank |
978-1-5386-8022-3 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kunyi Chen | 1 | 8 | 1.23 |
| Siyao Cheng | 2 | 438 | 22.59 |
| Hong Gao | 3 | 1086 | 120.07 |
| Jianzhong Li | 4 | 63 | 24.23 |