Name
Abstract | ||
|---|---|---|
Generally, a location-based service (LBS) often contains the location attribute, content attribute, time-stamp, and range. From the perspective of privacy protection, the location attribute and the content attribute are the key attributes and need to be protected. However, existing privacy protection methods focus excessively on the location attribute and ignore the content attribute contained in the LBS, which discloses the user's private information. In view of this challenge, a content-aware privacy protection method, called the CPP method that considers the content attribute is proposed. Specifically, the CPP method is based on using k-anonymity to generate dummy content attributes to protect the private content. As is shown in an experiment constructed on real-world data, the CPP method can indeed improve the effect of privacy protection. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1111/exsy.12907 | EXPERT SYSTEMS |
Keywords | DocType | Volume |
content privacy, k-anonymity, location-based service, privacy protection | Journal | 39 |
Issue | ISSN | Citations |
5 | 0266-4720 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiabang Liu | 1 | 0 | 0.34 |
| Xutong Jiang | 2 | 0 | 0.34 |
| Song Zhang | 3 | 0 | 0.34 |
| Bowen Liu | 4 | 7 | 2.44 |
| Wanchun Dou | 5 | 878 | 96.01 |