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Differential privacy–based location privacy enhancing in edge computing
Author(s) -
Miao Qiucheng,
Jing Weipeng,
Song Houbing
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4735
Subject(s) - quadtree , computer science , differential privacy , hilbert curve , edge computing , enhanced data rates for gsm evolution , node (physics) , data mining , theoretical computer science , algorithm , artificial intelligence , structural engineering , engineering
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%.