社交网络用户隐私量化研究:建模与实证分析

Research on quantifying user privacy on social networking sites

社交网络的流行对用户的隐私保护提出了新的挑战。该文通过使用人类动力学和统计物理的方法,研究用户的网络行为与用户隐私量值的关系。以当前国内流行的社交网络——人人网和新浪微博——为研究对象,获取用户的真实数据,提出隐私量化模型。研究结果表明:用户的网络行为对隐私量值具有重要的影响,如在人人网中用户的地理位置分享行为对隐私量值影响较大,而在新浪微博中发私信行为对隐私量值的影响最大。研究的结果对社交网络隐私关注下的用户行为规律探讨具有理论与实际意义。

The popularity of social networks puts forward new challenges on the user＇s privacy protection. In this paper, the methods of human dynamics and statistical physics were used to study the relationship of user＇s network behaviors and user＇s privacy value. Current domestic popular social networking sites, Renren and Sina Weibo, were used as the research objects, with the user＇s actual data then obtained and a privacy quantitative model proposed. The results show that the user＇s behaviors on the network have an important impact on the value of privacy. Renren＇s sharing behavior in the user＇s geographic location has great impact on privacy, while Sina microblogging sending private messages has the greatest impact on the value of privacy. Conclusions of this study have theoretical and practical significance.