Title: An encryption of social network user browsing trajectory data based on adversarial neural network

Authors: Xinliang Wang

Addresses: Binzhou Civil Air Defense Office, Binzhou 256600, Shandong, China; Binzhou Housing and Urban Rural Development Bureau, Binzhou 256600, Shandong, China

Abstract: In order to solve the problems of high information loss rate, poor encryption effect and long encryption time existing in traditional social network user browsing trajectory data encryption methods, this paper proposes an encryption method of social network user browsing trajectory data based on adversarial neural network. Mutual information is used to extract browsing characteristics of social network users and calculate browsing path similarity of social network users, so as to determine the clustering centre of browsing trajectory data and realise browsing trajectory data mining. Combining with adversarial neural network, the symmetric encryption and decoding model is designed, and the user browsing feature data is input into the model to realise the user browsing feature data encryption. Experimental results show that the information loss rate of the proposed method is always lower than 5%, the encryption effect is good, and the average encryption time is 53 ms.

Keywords: adversarial neural network; social network; user browsing trajectory; data encryption; data mining; symmetric encryption.

DOI: 10.1504/IJWBC.2024.136651

International Journal of Web Based Communities, 2024 Vol.20 No.1/2, pp.114 - 127

Received: 07 Mar 2022
Accepted: 09 Jun 2022
Published online: 15 Feb 2024 *

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