Multiple-negative survey method for enhancing the accuracy of negative survey-based cloud data privacy: Applications and extensions. (June 2017)

• Record Type: Journal Article
• Authors: Liu, Ran; Peng, Jinghui; Tang, Shanyu
• Is Part Of: Engineering applications of artificial intelligence. Volume 62(2017:Feb.)
• Page Start: 350
• Page End: 358
• Publication Date: 2017-06
• Subjects: Artificial immune system -- Cloud data privacy -- Multiple-negative survey -- Confidence level -- Bayes method -- Anonymity vote model; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2016.06.002
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: 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.