面向云数据的隐私度量研究进展

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四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与总结这四类方法的优缺点及其适用范围；最后，从隐私度量的过程、效果和方法三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。

Research Progress on Privacy Measurement for Cloud Data

Privacy protection technologyisan important guarantee to prevent theprivacy disclosure of sensitive information in the cloud computing environment. In order to design better privacy protection schemes, we require the privacy measurement technique which reflects 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 the privacy measurement for the cloud data. Firstly, we introduceanoverview of the privacy protection and privacy measurement,give some quantitative methodsof the background knowledgefor the attacks, andgive some performance evaluation indexes and a comprehensiveevaluationframework of the privacy protection schemesfor the cloud data.Moreover, we propose an abstract model of the privacy measurementfor the cloud data, and elaborately describe the existing privacy measurement methods based on anonymity, information entropy, set pair analysis theory and differential privacy respectively from the perspective of working principle and the specific implementation.Furthermore, we analysis and summarytheir advantages and disadvantages andthe application scopes of the above four types of privacy measurement methods by the privacy measurementindexes and effectiveness. Finally, we summarize the development trends and the future problems of the privacy measurement for the cloud data in terms of theprivacy measurement processes, effects and methods.