Name
Abstract | ||
|---|---|---|
K-anonymity has been gaining widespread attention as one of the most widely used technologies to protect location privacy. Nevertheless, there are still some threats such as behavior deception and service swing, since utilizing distributed k-anonymity technology to construct an anonymous domain. More specifically, the coordinate of the honest node will be a leak if the malicious nodes submit wrong locations coordinate to take part in the domain construction process. Worse still, owing to service swing, the attacker increases the reputation illegally to deceive honest nodes again. To overcome those drawbacks, we propose a trusted de-swinging k-anonymity scheme for location privacy protection. Primarily, we introduce a de-swinging reputation evaluation method (DREM), which designs a penalty factor to curb swinging behavior. This method calculates the reputation from entity honesty degree, location information entropy, and service swing degree. Besides, based on our proposed DREM, a credible cloaking area is constructed to protect the location privacy of the requester. In the area, nodes can choose some nodes with a high reputation for completing the construction process of the anonymous domain. Finally, we design reputation contracts to calculate credit automatically based on smart contracts. The security analysis and simulation results indicate that our proposed scheme effectively resists malicious attacks, curbs the service swing, and encourages nodes to participate honestly in the construction of cloaking areas. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1186/s13677-021-00272-4 | Journal of Cloud Computing |
Keywords | DocType | Volume |
k-Anonymity, De-swinging, Location privacy protection, Reputation evaluation, Smart contracts | Journal | 11 |
Issue | ISSN | Citations |
1 | 2192-113X | 0 |
PageRank | References | Authors |
0.34 | 28 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manxiang Yang | 1 | 0 | 0.34 |
| Baopeng Ye | 2 | 0 | 0.34 |
| Yuling Chen | 3 | 27 | 10.33 |
| Tao Li | 4 | 29 | 6.40 |
| Yixian Yang | 5 | 1121 | 140.62 |
| Xiaobin Qian | 6 | 0 | 0.34 |
| Xiaomei Yu | 7 | 14 | 4.01 |