Name
Abstract | ||
|---|---|---|
As the smart devices and cloud services are rapidly expanding, a large amount of location information can easily be gathered. However, there is a conflict between collecting location information and protecting personal information since obtaining and utilizing the information may be restricted due to privacy concerns. In fact, various methods which use K-anonymity for original location data have been studied, but these methods have excessively reduced data utility while stressing highly on privacy preservation. In this research, we suggest a novel model to overcome this fundamental dilemma. Compared to the existing approaches, our study shows a new theoretical advancement in privacy protection and outstanding performance in terms of time complexity and data utility. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/Cybermatics_2018.2018.00170 | 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) |
Keywords | Field | DocType |
Trajectory,Privacy,Databases,Data privacy,Data models,Publishing,Couplings | Data modeling,Computer science,Computer security,Personally identifiable information,Data publishing,Dilemma,Publishing,Information privacy,Time complexity,Cloud computing | Conference |
ISBN | Citations | PageRank |
978-1-5386-7975-3 | 1 | 0.35 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chris Soo-Hyun Eom | 1 | 31 | 3.41 |
| Wookey Lee | 2 | 196 | 29.22 |
| Carson K. Leung | 3 | 1625 | 115.64 |