Differential privacy–based location privacy enhancing in edge computing. (29th June 2018) - Miao, Qiucheng; Jing, Weipeng; Song, Houbing
Summary: In the era of edge computing, real‐time data preprocessing on the edge node has the potential to improve computational efficiency and data accuracy. However, a significant challenge is private data disclosure, particularly in the case of location‐based services. To address this challenge, in this paper, by leveraging differential privacy, we propose a privacy‐aware framework for mobile edge computing called MEPA to protect the location privacy in which the edge node is regarded as an anonymous central server. The proposed framework can provide computing services without deploying special infrastructure. To be specific, in order to solve the problem of constrained computing resources in the edge nodes, the algorithm of Quadtree Differential Privacy based on Hilbert curve division (QTDP‐H) two‐dimensional spatial data query transmission is proposed. First, a noise quadtree is established and the privacy budget is divided according to the tree level. Then, the constructed quadtree is represented by quanternary, so that the partition based on Hilbert curve can be established and the two‐dimensional data in the area can be converted into one‐dimensional, which can greatly improve the retrieval efficiency. The effectiveness of the proposed algorithm in terms of time complexity and retrieval accuracy has been verified by extensive experimental results. Compared with traditional methods of ( D, ε ) −  L P, the average runtime can be reduced by 15%‐20%, and the average relative error is reduced by 20%.