Title: The study of PGP web of trust based on social network analysis

Authors: Victor Chang; Lina Xiao; Anastasija Nikiforova; Qianwen Ariel Xu; Ben S.C. Liu

Addresses: Artificial Intelligence and Information Systems Research Group, School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK ' IBSS, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China ' University of Latvia, Raina Boulevard 19, LV-1050, Riga, Latvia ' Artificial Intelligence and Information Systems Research Group, School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK; IBSS, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China ' School of Business, Quinnipiac University, Hamden, Connecticut 06518, USA

Abstract: This paper aims to explore the patterns of online interaction of users of the pretty good privacy (PGP) algorithm to identify the most important and influential users in the social network. While PGP is widely used in protecting email privacy, there are some encryption defects that can raise users' concerns about data privacy and security. It is therefore essential to identify the most influential and active users who are trusted widely, getting numerous keys in the PGP web of trust. However, it is not always known whether the user actually gained trust from others or it is one who illegally forged the keys. In order to identify the most important users in the PGP network, social network analysis (SNA) is used to analyse their online interaction conditions. Along with the most traditional centrality analysis, a less frequent used K-means clustering analysis is also conducted to obtain more precise and accurate results. The SNA results show that: 1) PGP algorithm users' online interaction patterns are rather different, which include both frequent versus isolated; 2) people with higher centrality use the PGP algorithm more frequently and may become the target peeks to seek; 3) in the analysed network, all important nodes are in the same cluster when applying K-means model to divide the community.

Keywords: online interaction; online community; pretty good privacy; PGP; web of trust; social network analysis; SNA; centrality analysis; K-means clustering analysis.

DOI: 10.1504/IJBIS.2023.134956

International Journal of Business Information Systems, 2023 Vol.44 No.2, pp.285 - 302

Received: 12 Aug 2020
Accepted: 28 Oct 2020
Published online: 22 Nov 2023 *

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