大数据及其隐私保护

方贤进; 肖亚飞; 杨高明

doi:10.11959/j.issn.2096-0271.2017051

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大数据及其隐私保护

专题：大数据安全和隐私保护 | 更新时间：2024-06-03

- 大数据及其隐私保护
- Privacy preserving in the age of big data
- 大数据 2017年3卷第5期页码：2017051
- 作者机构：
- 作者简介：
  
  [ "方贤进（1970-），男，博士，安徽理工大学计算机科学与工程学院教授，主要研究方向为网络与信息安全、智能计算。" ]
  [ "肖亚飞（1994-），男，安徽理工大学计算机科学与工程学院硕士生，主要研究方向为隐私保护。" ]
  [ "杨高明（1974-），男，博士，安徽理工大学计算机科学与工程学院副教授，主要研究方向为隐私保护。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61572034);国家自然科学基金资助项目;The National Natural Science Foundation of China(61402012)
- DOI：10.11959/j.issn.2096-0271.2017051
  中图分类号： TP311
- 网络首发：2017-09，
  
  纸质出版：2017-09-20
- 稿件说明：
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方贤进, 肖亚飞, 杨高明. 大数据及其隐私保护[J]. 大数据, 2017,3(5):2017051.

Xianjin FANG, Yafei XIAO, Gaoming YANG. Privacy preserving in the age of big data[J]. Big data research, 2017, 3(5): 2017051.
方贤进, 肖亚飞, 杨高明. 大数据及其隐私保护[J]. 大数据, 2017,3(5):2017051. DOI： 10.11959/j.issn.2096-0271.2017051.

Xianjin FANG, Yafei XIAO, Gaoming YANG. Privacy preserving in the age of big data[J]. Big data research, 2017, 3(5): 2017051. DOI： 10.11959/j.issn.2096-0271.2017051.

摘要

在对大数据进行发布或数据挖掘的过程中，隐私泄露是人们最关心的问题，但目前关于大数据隐私保护的研究还处在初级阶段。介绍了有关隐私保护系统的基础知识，包括数据参与角色与数据操作的定义，给出了隐私保护系统的数学描述与隐私度量方法，分析了隐私保护的数学模型，包括k-匿名模型与差分隐私模型。回顾了基于位置服务的隐私保护及其应用，总结了大数据时代隐私保护的挑战与机遇，指出了用于改进现有隐私保护方法的研究方向，以满足大数据前所未有的各种计算需求。

Abstract

One of biggest concerns of big data is privacy

especially

in the processing of big data publishing or data mining.However

the study on big data privacy is still at a very early stage.The preliminary knowledge about the definition of roles and operations of privacy system were introduced.The mathematical description and measurement metrics of privacy study was given.The models of privacy preserving were analyzed

including k-anonymity and differential privacy.The current situation of privacy preserving in big data age was reviewed

especially

the privacy preserving based location-based services and its applications were summarized.The challenges and opportunities in the age of big data were summarized.The directions to improve the existing privacy protection methods satisfying the unprecedented computational requirements of big data were pointed out.

关键词

Keywords

references

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