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

Wang, Tao and Zheng, Zhigao and Rehmani, Mubashir Husain and Yao, Shihong and Huo, Zheng (2019) Privacy preservation in big data from the communication perspective—A survey. IEEE Communications Surveys and Tutorials, 21 (1). pp. 753-778. ISSN 1553-877X

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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.

Item Type:	Article
Additional Information:	Funding Information: Manuscript received March 29, 2018; revised July 16, 2018; accepted August 7, 2018. Date of publication August 13, 2018; date of current version February 22, 2019. This work was supported in part by the Fundamental Research Funds for the Central Universities under Grant 2042017kf0044, in part by the China Post-Doctoral Science Foundation under Grant 2017M612511, in part by the National Natural Science Foundation of China under Grant 61701453 and Grant 41671443, and in part by LIESMARS Special Research Funding. (Corresponding author: Zhigao Zheng.) T. Wang is with the Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China, and also with the State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China (e-mail: wangtao.mac@whu.edu.cn). Funding Information: This work was supported in part by the Fundamental Research Funds for the Central Universities under Grant 2042017kf0044, in part by the China Post-Doctoral Science Foundation under Grant 2017M612511, in part by the National Natural Science Foundation of China under Grant 61701453 and Grant 41671443, and in part by LIESMARS Special Research Funding. Publisher Copyright: © 2018 IEEE.
Uncontrolled Keywords:	/dk/atira/pure/subjectarea/asjc/2200/2208
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Depositing User:	Admin SSL
Date Deposited:	19 Oct 2022 23:11
Last Modified:	14 Aug 2023 16:05
URI:	http://repository-testing.wit.ie/id/eprint/4587

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