Multiple-negative survey method for enhancing the accuracy of negative survey-based cloud data privacy: Applications and extensions
Multiple-negative survey method for enhancing the accuracy of negative survey-based cloud data privacy: Applications and extensions
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提高基于负面调查的云数据隐私准确性的多重负面调查方法：应用与扩展
DOI：
10.1016/j.engappai.2016.06.002
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发表时间：
2017
影响因子：
8
通讯作者：
Shanyu Tang
中科院分区：
文献类型：
--
作者：
Ran Liu（参与人员）;Jinhui Peng;Shanyu Tang
云计算以其高效率、可用性、可访问性和可负担性为人们的生活带来了便利。但云数据的隐私面临着严峻的挑战。受人工免疫系统（AIS）启发的负面调查虽然可以高效、隐私保护程度高地保护用户的隐私数据，但其准确性受到客户端数量的影响，客户端数量不足可能会导致较大的误差。本研究的重点是弥补这一弱点的多重负面调查方法。与传统的负面调查方法相比，多重负面调查方法收集每个用户的多个不同的负面类别，而不是仅收集一个负面类别。分析了两个关键的科学问题（准确性和置信度），然后提出了基于多重否定调查方法的应用（匿名投票模型）。
Cloud computing brings convenience to people's lives because of its high efficiency, usability, accessibility and affordability. But the privacy of cloud data faces severe challenges. Although negative survey, which is inspired by Artificial Immune System (AIS), can protect users' privacy data with high efficiency and degree of privacy protection, its accuracy is influenced by the number of client terminals, and insufficient client terminals may lead to large errors. This study focuses on a multiple-negative survey method of remedying this weakness. Compared with the traditional negative survey method, the multiple-negative survey method collects each user's multiple different negative categories rather than only one negative category. Two key scientific problems (accuracy and confidence level) are analyzed, and an application (anonymity vote model) is then proposed based on the multiple-negative survey method.