Big Privacy: Challenges and Opportunities of Privacy Study in the Age of Big Data

被引:183
|
作者
Yu, Shui [1 ]
机构
[1] Deakin Univ, Sch Informat Technol, Waurn Ponds, Vic 3216, Australia
来源
IEEE ACCESS | 2016年 / 4卷
基金
中国国家自然科学基金;
关键词
Big data; privacy; data clustering; differential privacy; IDENTIFICATION; INFORMATION; MECHANISM; SYSTEMS; UNIQUE;
D O I
10.1109/ACCESS.2016.2577036
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
One of the biggest concerns of big data is privacy. However, the study on big data privacy is still at a very early stage. We believe the forthcoming solutions and theories of big data privacy root from the in place research output of the privacy discipline. Motivated by these factors, we extensively survey the existing research outputs and achievements of the privacy field in both application and theoretical angles, aiming to pave a solid starting ground for interested readers to address the challenges in the big data case. We first present an overview of the battle ground by defining the roles and operations of privacy systems. Second, we review the milestones of the current two major research categories of privacy: data clustering and privacy frameworks. Third, we discuss the effort of privacy study from the perspectives of different disciplines, respectively. Fourth, the mathematical description, measurement, and modeling on privacy are presented. We summarize the challenges and opportunities of this promising topic at the end of this paper, hoping to shed light on the exciting and almost uncharted land.
引用
收藏
页码:2751 / 2763
页数:13
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