In order to obtain social benefits, social networks have started taking benefits from private information of network users. While having increased concerns about the risk of privacy disclosure, users still generally disclosed under high privacy concerns, which directly formed the privacy paradox. The expansion and generalization of privacy paradox indicate that the implementation of privacy protection in social networks is still in a dilemma. Studying and solving the problem of privacy paradox is conducive to ensure the healthy development of social network industry. Based on this, this study has designed a research system that analyzes the privacy paradox of social networks from three dimensions: cause, existence and form. After studying existing research of privacy paradox in social networks, evolutionary game theory is determined to be introduced into the procedure of cause analysis, while data mining is used as a data analysis method for empirical research. Within the whole research process, the evolutionary game model of privacy paradox in social networks is built up first, while the necessary conditions for the generation of privacy paradox is addressed, which is derived from the evolutionary stable strategy. Secondly, the questionnaire survey method is used to collect private data of active users of both Weibo and WeChat. Lastly, Apriori and CHAID algorithm are used to determine the relationship of user privacy concerns, privacy behavior, and other factors, which then confirms the existence of privacy paradox on two social networks and makes a comparison between their forms of privacy paradox in specific. This research systematically makes a useful an in-depth analysis to the privacy paradox in social networks and is meaningful for establishing a hierarchical protection system of users' privacy for enterprises.
Huang, Yijun; Lu, Tong; Cheng, Kaige; and Wang, Jiayu, "Research on Privacy Paradox in Social Networks Based on Evolutionary Game Theory and Data Mining" (2020). WHICEB 2020 Proceedings. 19.