Liu, Ran, Peng, Jinghui and Tang, Shanyu ORCID: https://orcid.org/0000-0002-2447-8135 (2017) Multiple-negative survey method for enhancing the accuracy of negative survey-based cloud data privacy: applications and extensions. Engineering Applications of Artificial Intelligence, 62. pp. 350-358. ISSN 0952-1976

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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.

Item Type:	Article
Identifier:	10.1016/j.engappai.2016.06.002
Additional Information:	© 2016 Elsevier Ltd. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Keywords:	Artificial immune system; Cloud data privacy; Multiple-negative survey; Confidence level; Bayes method; Anonymity vote model
Subjects:	Computing > Information security > Cyber security Computing > Information security
Depositing User:	Shanyu Tang
Date Deposited:	28 Jul 2017 17:43
Last Modified:	04 Nov 2024 12:06
URI:	https://repository.uwl.ac.uk/id/eprint/3644

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