社交网络用户隐私泄露量化评估方法

社交网络用户隐私泄露的量化评估有利于帮助用户了解个人隐私泄露状况,提高公众隐私保护和防范意识,同时也能为个性化隐私保护方法的设计提供依据.针对目前隐私量化评估方法主要用于评估隐私保护方法的保护效果,无法有效评估社交网络用户的隐私泄露风险的问题,提出了一种社交网络用户隐私泄露量化评估方法.基于用户隐私偏好矩阵,利用皮尔逊相似度计算用户主观属性敏感性,然后取均值得到客观属性敏感性;采用属性识别方法推测用户隐私属性,并利用信息熵计算属性公开性;通过转移概率和用户重要性估计用户数据的可见范围,计算数据可见性;综合属性敏感性、属性公开性和数据可见性计算隐私评分,对隐私泄露风险进行细粒度的个性化评估,同时考虑时间因素,支持用户隐私泄露状况的动态评估,为社交网络用户了解隐私泄露状况、针对性地进行个性化隐私保护提供支持.在新浪微博数据上的实验结果表明,所提方法能够有效地对用户的隐私泄露状况进行量化评估.

A quantitative evaluation method of social network users ' privacy leakage

Quantitative assessment of social network users’ privacy leakage can help users’ understand personal privacy, improve public privacy protection and prevention awareness, and also provide a basis for the design of personalized privacy protection methods. Current privacy quantitative assessment methods are mainly used to evaluate the protective effect of privacy protection methods, and are not able to effectively assess the privacy leakage risk of social network users. A quantitative evaluation method of social network users’ privacy leakage is proposed. Firstly, users’ subjective attribute sensitivity is calculated by Pearson similarity based on privacy preference matrix, and is averaged to obtain the objective attribute sensitivity. Attribute openness is calculated by the information entropy of posterior distribution which is inferenced by user sensitive attribute inference method. Transition probability and user importance is used to estimate the visible range of user data to calculate data visibility. Then, privacy score is calculated by aggregating attribute sensitivity, attribute openness, and data visibility. Finally, a fine-grained privacy evaluation is conducted based on user's privacy score, which supports dynamic evaluation of user privacy and provides a basis for personalized privacy protection. The experimental results on Sina Weibo data show that the proposed method can effectively quantify the user's privacy leakage status.