Title: Privacy protection of multiple sensitive attribute data for users on e-commerce social media platforms

Authors: Na Wang; Ji Zhang; Feng Gao

Addresses: School of Management, Changchun University of Architecture and Civil Engineering, Chang'chun, 130607, China ' School of Electrical Engineering, Changchun University of Architecture and Civil Engineering, Chang'chun, 130607, China ' School of Art, Changchun University of Architecture and Civil Engineering, Chang'chun, 130607, China

Abstract: Protecting the privacy of multi-sensitive attribute data is of great significance for safeguarding the interests of individuals, enterprises, and countries, as well as promoting technological development. Therefore, this paper proposes a privacy protection method of multiple sensitive attribute data for users on e-commerce social media platforms. An improved artificial bee colony algorithm is used to improve the KHM algorithm and mine multi-sensitive attribute data. Based on a personalised anonymity model, the mined multi-sensitive attribute data is quantified in a graded manner according to the sensitivity value and user privacy requirements of each attribute data. Low-sensitive attribute data is protected by equivalent expression privacy protection, while high-sensitive attribute data is encrypted and hidden by an improved fully homomorphic encryption method, thus achieving data privacy protection. The experimental results show that the probability of successful abnormal extraction of data by hackers using this method is less than 0.05, which improves privacy security.

Keywords: e-commerce; multiple sensitive; attribute data; privacy protection; KHM algorithm; social media.

DOI: 10.1504/IJWBC.2024.142479

International Journal of Web Based Communities, 2024 Vol.20 No.3/4, pp.278 - 297

Received: 25 May 2023
Accepted: 10 Oct 2023
Published online: 04 Nov 2024 *

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