Survey on Local Differential Privacy

被引：0
|
作者：
Ye Q.-Q. [1 ]
Meng X.-F. [1 ]
Zhu M.-J. [1 ]
Huo Z. [2 ]
机构：
[1] School of Information, Renmin University of China, Beijing
[2] School of Information Technology, Hebei University of Economics and Business, Shijiazhuang
来源：
Ruan Jian Xue Bao/Journal of Software | 2018年 / 29卷 / 07期
基金：
国家重点研发计划; 中国国家自然科学基金;
关键词：
Centralized differential privacy; Local differential privacy; Privacy preserving;
D O I：
10.13328/j.cnki.jos.005364
中图分类号：
学科分类号：
摘要：
With the development of information technology in the big data era, there has been a growing concern for privacy of personal information. Privacy preserving is a key challenge when releasing and analyzing data. Centralized differential privacy is based on the assumption of a trustworthy data collector; however, it is actually a bit difficult to realize in practice. To address this issue, local differential privacy has emerged as a new model for privacy preserving with strong privacy guarantees. By resisting adversaries with any background knowledge and preventing attacks from untrustworthy data collector, local differential privacy can protect private information thoroughly. Starting with an introduction to the mechanisms and properties, this paper surveys the state of the art of local differential privacy, focusing on the frequency estimation, mean value estimation and the design of perturbation model. Following a comprehensive comparison and analysis of existing techniques, further research challenges are put forward. © Copyright 2018, Institute of Software, the Chinese Academy of Sciences. All rights reserved.
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页码：1981 / 2005
页数：24
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