Name
Abstract | ||
|---|---|---|
The burgeoning technology of Mobile Edge Computing is attracting the traditional LBS and LS to deploy due to its nature characters such as low latency and location awareness. Although this transplant will avoid the location privacy threat from the central cloud provider, there still exists the privacy concerns in the LS of MEC scenario. Location privacy threat arises during the procedure of the fingerprint localization, and the previous studies on location privacy are ineffective because of the different threat model and information semantic. To address the location privacy in MEC environment, we designed LoPEC, a novel and effective scheme for protecting location privacy for the MEC devices. By the proper model of the RAN access points, we proposed the noise-addition method for the fingerprint data, and successfully induce the attacker from recognizing the real location. Our evaluation proves that LoPEC effectively prevents the attacker from obtaining the useru0027s location precisely in both single-point and trajectory scenarios. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Cryptography and Security | Computer security,Computer science,Threat model,Cloud provider,Fingerprint,Mobile edge computing,Latency (engineering),Pound (mass),Location awareness,Trajectory |
DocType | Volume | Citations |
Journal | abs/1804.01636 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongli Zhang | 1 | 267 | 41.85 |
| Yuhang Wang | 2 | 1 | 1.40 |
| X. Du | 3 | 2320 | 241.73 |
| Mohsen Guizani | 4 | 6456 | 557.44 |