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| 面向云数据的隐私度量研究进展 |
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熊金波1,2,3, 王敏燊1, 田有亮2, 马蓉1, 姚志强1,3, 林铭炜1
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1.福建师范大学 数学与信息学院, 福建 福州 350117;2.贵州省公共大数据重点实验室(贵州大学), 贵州 贵阳 550025;3.福建省网络安全与密码技术重点实验室, 福建 福州 350007
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| 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. |
| 关键词: 隐私泄露 隐私度量 数据隐私 隐私保护 差分隐私 |
| DOI：10.13328/j.cnki.jos.005363 |
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| 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） |
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| Research Progress on Privacy Measurement for Cloud Data |
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XIONG Jin-Bo1,2,3, WANG Min-Shen1, TIAN You-Liang2, MA Rong1, YAO Zhi-Qiang1,3, LIN Ming-Wei1
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1.College of Mathematics and Informatics, Fujian Normal University, Fuzhou 350117, China;2.Guizhou Provincial Key Laboratory of Public Big Data(Guizhou University), Guiyang 550025, China;3.Fujian Provincial Key Laboratory of Network Security and Cryptology, Fujian Normal University, Fuzhou 350007, China
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| 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. |
| Key words: privacy disclosure privacy measurement data privacy privacy protection differential privacy |
