Recently, with an increase in Internet usage, users of online social networks (OSNs) have increased. Consequently, privacy leakage has become more serious. However, few studies have investigated the difference between privacy and actual behaviors. In particular, users' desire to change their privacy status is not supported by their privacy literacy. Presenting an accurate measurement of users' privacy status can cultivate the privacy literacy of users. However, the highly interactive nature of interpersonal communication on OSNs has promoted privacy to be viewed as a communal issue. As a large number of redundant users on social networks are unrelated to the user's privacy, existing algorithms are no longer applicable. To solve this problem, we propose a structural similarity measurement method suitable for the characteristics of social networks. The proposed method excludes redundant users and combines the attribute information to measure the privacy status of users. Using this approach, users can intuitively recognize their privacy status on OSNs. Experiments using real data show that our method can effectively and accurately help users improve their privacy disclosures.

中文翻译：

---

在线社交网络上使用图结构的隐私测量方法

最近，随着互联网使用量的增加，在线社交网络 (OSN) 的用户也有所增加。因此，隐私泄露变得更加严重。然而，很少有研究调查隐私和实际行为之间的差异。尤其是，用户想要改变他们的隐私状态的愿望并没有得到他们的隐私素养的支持。准确衡量用户的隐私状况，可以培养用户的隐私素养。然而，OSN 上人际交流的高度交互性促使隐私被视为一个公共问题。由于社交网络上大量冗余用户与用户隐私无关，现有算法不再适用。为了解决这个问题，我们提出了一种适合社交网络特征的结构相似度测量方法。所提出的方法排除冗余用户并结合属性信息来衡量用户的隐私状态。使用这种方法，用户可以直观地识别他们在 OSN 上的隐私状态。使用真实数据的实验表明，我们的方法可以有效、准确地帮助用户改善他们的隐私披露。