| Autor: |
Yuan Tian, Biao Song, Mznah Al Rodhaan, Chen Rong Huang, Mohammed A. Al-Dhelaan, Abdullah Al-Dhelaan, Najla Al-Nabhan |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
Mathematical Biosciences and Engineering, Vol 17, Iss 3, Pp 2636-2649 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
1551-0018 |
| DOI: |
10.3934/mbe.2020144?viewType=HTML |
| Popis: |
Location-based Service has become the fastest growing activity related service that people use in their daily life due to the boom of location-aware mobile devices. In edge computing along with the benefits brought by LBS, privacy preservation becomes a more challenging issue because of the nature of the paradigm, in which peers may cooperate with each other to collect and analyze user's location data. To avoid potential information leakage and usage, user's exact location should not be exposed to the edge node. In this paper, we propose a stochastic location privacy protection scheme for edge computing, in which the geographical distribution of surrounding users is obtained by analyzing proposed long-term density map and short-term density map. The cloaking scheme transfers user's exact location to a cloaked location to satisfy predefined probability of having k-users in that area. Our scheme does not reveal any exact location information, thus it is practicable for the real scenario when edge computing is honest but curious. Extensive experimental results are conducted to verify the efficiency and effectiveness of our method. By varying the privacy protection requirements, the corresponding performance have been examined and discussed. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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