| Title |
Group-Oriented Location Privacy Protection For Mobile Users |
| ID_Doc |
28528 |
| Authors |
Ji Y.; Gui R.; Gui X.; Dai H. |
| Year |
2020 |
| Published |
Proceedings - 2020 IEEE 22nd International Conference on High Performance Computing and Communications, IEEE 18th International Conference on Smart City and IEEE 6th International Conference on Data Science and Systems, HPCC-SmartCity-DSS 2020 |
| DOI |
http://dx.doi.org/10.1109/HPCC-SmartCity-DSS50907.2020.00135 |
| Abstract |
In order to achieve privacy protection without reducing the availability of data, it is required to diminish the location data error between the location data before and after privacy protection while maintaining the characteristics of groups of user's location. Aiming at the above requirement, we realize the group-oriented location privacy based on the k-Anonymity algorithm and the grid method in crowdsensing task assignment. Firstly, the initial grid size is determined based on the overall density of users in the target area, which is to be divided into grid units. The number of users in each grid unit is calculated, whose relationship with the anonymous parameter k affects the next operation to grid units. Secondly, the grid mergence is conducted on those selected by the heuristic search based on the heuristic search of the clustering result. Then, the grid division is carried out on the grids which need to be divided by means of equilibrium segmentation based on geographic midline. Finally, the anonymous region is created for the grid area satisfying the k-Anonymity requirement, achieving the k-Anonymity location privacy protection. Experiments show that our method can minimize the location deviation while keep the privacy protection intensity, improving the service quality in crowdsensing. © 2020 IEEE. |
| Author Keywords |
crowdsensing; k-Anonymity; location privacy protection; rasterization |
