Privacy preservation in big data from the communication perspective—A survey

Tao Wang, Zhigao Zheng, Mubashir Husain Rehmani, Shihong Yao, Zheng Huo

Research output: Contribution to journalArticlepeer-review

37 Citations (Scopus)

Abstract

The advancement of data communication technologies promotes widespread data collection and transmission in various application domains, thereby expanding big data significantly. Sensitive information about individuals, which is typically evident or hidden in data, is prone to various privacy attacks and serious risks of privacy disclosure. Corresponding approaches to data privacy preservation have been proposed to provide mechanisms for preserving data privacy while pubilishing useful information or mining valuable information from sanitized data. In this work, we present a comprehensive survey of privacy preservation in big data from the communication perspective. Specifically, we cover the fundamental privacy-preserving framework and privacy-preserving technologies, particularly differential privacy. We also survey the adaptations and variants of differential privacy for different emerging applications and the challenges to differential privacy. In addition, we provide future research directions about privacy preservation in communication field.

Original languageEnglish
Article number8433900
Pages (from-to)753-778
Number of pages26
JournalIEEE Communications Surveys and Tutorials
Volume21
Issue number1
DOIs
Publication statusPublished - 01 Jan 2019

Keywords

  • Background information
  • Data communication
  • Differential privacy
  • Indistinguishability
  • Privacy

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