面向云数据的隐私度量研究进展
作者:
作者单位:

作者简介:

熊金波(1981-),男,湖南益阳人,博士,副教授,CCF专业会员,主要研究领域为云数据安全,隐私保护技术;马蓉(1992-),女,硕士生,CCF学生会员,主要研究领域为云数据安全,隐私保护技术;王敏燊(1994-),男,硕士生,CCF学生会员,主要研究领域为信任评估,数据安全;姚志强(1967-),男,博士,教授,CCF高级会员,主要研究领域为信息安全;田有亮(1982-),男,博士,教授,博士生导师,主要研究领域为算法博弈论,数据安全,隐私保护;林铭炜(1985-),男,博士,副教授,CCF专业会员,主要研究领域为存储系统,嵌入式系统.

通讯作者:

姚志强,E-mail:yzq@fjnu.edu.cn

中图分类号:

基金项目:

国家自然科学基金(61772008,61502102,61370078,61363068);福建省自然科学基金(2015J05120,2016J05149,2017J05099);贵州省公共大数据重点实验室开放课题基金(2017BDKFJJ028);福建省高校杰出青年科研人才培育计划(2015,2017);贵州省科技拔尖人才项目(黔教合KY[2016]060)


Research Progress on Privacy Measurement for Cloud Data
Author:
Affiliation:

Fund Project:

National Natural Science Foundation of China (61772008, 61502102, 61370078, 61363068); Natural Science Foundation of Fujian Province, China (2015J05120, 2016J05149, 2017J05099); Guizhou Provincial Key Laboratory of Public Big Data Research Fund (2017BDKFJJ 028); Distinguished Young Scientific Research Talents Plan in Universities of Fujian Province (2015, 2017); Science and Technology Top-Notch Talent Support Project in Guizhou Province Department of Education (黔教合KY[2016]060)

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    隐私保护技术是云计算环境中防止隐私信息泄露的重要保障,通过度量这种泄露风险可反映隐私保护技术的隐私保护强度,以便构建更好的隐私保护方案.因此,隐私度量对隐私保护具有重大意义.主要对现有面向云数据的隐私度量方法进行综述:首先,对隐私保护技术和隐私度量进行概述,给出攻击者背景知识的量化方法,提出云数据隐私保护技术的性能评价指标和一种综合评估框架;然后,提出一种云数据隐私度量抽象模型,从工作原理和具体实施的角度对基于匿名、信息熵、集对分析理论和差分隐私这4类隐私度量方法进行详细阐述;再从隐私度量指标和度量效果方面分析和总结这4类方法的优缺点及其适用范围;最后,从隐私度量的过程、效果和方法这3个方面指出云数据隐私度量技术的发展趋势及有待解决的问题.

    Abstract:

    Privacy protection technology is an important guarantee to prevent the privacy disclosure of sensitive information in the cloud computing environment. In order to design better privacy protection schemes, a privacy measurement technique is required that can reflect the privacy protection intensity by measuring the disclosure risk of privacy information in the privacy protection schemes. Therefore, privacy measurement is of great significance for the privacy protection of the cloud data. This paper systematically reviews the existing methods of privacy measurement for the cloud data. Firstly, an overview of the privacy protection and privacy measurement is provided along with descriptions of some quantitative methods of the background knowledge for the attacks, some performance evaluation indexes and a comprehensive evaluation framework of the privacy protection schemes for the cloud data. Moreover, an abstract model of the privacy measurement for the cloud data is proposed, and the existing privacy measurement methods are elaborated based on anonymity, information entropy, set pair analysis theory and differential privacy respectively from the perspective of working principle and the specific implementation. Furthermore, the advantages and disadvantages and the application scopes of the above four types of privacy measurement methods are analyzed by the privacy measurement indexes and effectiveness. Finally, the development trends and the future problems of the privacy measurement for the cloud data are summarized in terms of the privacy measurement processes, effects and methods.

    参考文献
    相似文献
    引证文献
引用本文

熊金波,王敏燊,田有亮,马蓉,姚志强,林铭炜.面向云数据的隐私度量研究进展.软件学报,2018,29(7):1963-1980

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2017-05-30
  • 最后修改日期:2017-08-22
  • 录用日期:
  • 在线发布日期: 2017-10-17
  • 出版日期:
您是第位访问者
版权所有:中国科学院软件研究所 京ICP备05046678号-3
地址:北京市海淀区中关村南四街4号,邮政编码:100190
电话:010-62562563 传真:010-62562533 Email:jos@iscas.ac.cn
技术支持:北京勤云科技发展有限公司

京公网安备 11040202500063号