A Differential Privacy-Based Query Model for Sustainable Fog Data Centers

Miao Du, Kun Wang, Xiulong Liu, Song Guo, Yan Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

24 Citations (Scopus)

Abstract

With the increasing computation and storage capabilities of mobile devices, the concept of fog computing was proposed to tackle the high communication delay inherent in cloud computing, and also improve the security to some extent. This paper concerns with the privacy issue inherent in the sustainable fog computing platform. However, there is no universal solution to the privacy problem in fog computing due to the device heterogeneity. In this paper, we proposed a differential privacy-based query model for sustainable fog computing supported data center. We designed a method that can quantify the quality of privacy preserving through rigorous mathematical proof. The proposed method uses the query model to capture the structure information of the sustainable fog computing supported data center, and the datasets for the query result are mapped to real vectors. Then, we implemented the differential privacy preserving by injecting Laplacian noise. The experiment results demonstrated that the proposed method can effectively resist various popular privacy attacks, and achieve relatively high data utility under the premise of better privacy preserving.

Original languageEnglish
Article number7947232
Pages (from-to)145-155
Number of pages11
JournalIEEE Transactions on Sustainable Computing
Volume4
Issue number2
DOIs
Publication statusPublished - 1 Apr 2019

Keywords

  • data center
  • Differential privacy
  • fog computing
  • Laplacian mechanism
  • query model

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Hardware and Architecture
  • Software
  • Renewable Energy, Sustainability and the Environment
  • Control and Optimization

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