Privacy scoring aims at measuring the privacy violation risk of a user over an online social network (OSN). Existing work in the field rely on possibly biased or emotional survey data and focus only on personel purpose OSNs like Facebook. In contrast to existing work, in this thesis, we work with real-world OSN data collected from LinkedIn, the most popular professional-purpose OSN (ProOSN). Towards this end, we developed an extensive crawler to collect all relevant profile data of 5,389 LinkedIn users, modelled these data using both relational and graph databases and quantitatively analyzed all privacy risk scoring methods in the literature. Additionally, we propose a novel scoring method that consider the granularity of data an OSN user shares on her profile page. Extensive experimental evaluation of existing and proposed scoring methods indicates the effectiveness of the proposed solution.
翻译：隐私评分旨在衡量用户在网上社交网络上侵犯隐私的风险。实地的现有工作依靠可能带有偏见或情感色彩的调查数据,只关注Facebook等个人目的的OSN。与目前的工作相比,在本论文中,我们使用从最受欢迎的专业目的OSN(ProOSN)LinkedIn(LinkedIn)收集的真实世界的OSN数据。为此,我们开发了一个广泛的爬行器,收集5 389 LinkedIn用户的所有相关概况数据,利用关系和图表数据库对这些数据进行模拟,并对文献中的所有隐私风险评分方法进行定量分析。此外,我们提出了一个新的评分方法,其中考虑到数据颗粒性,即OSN用户在她简介页上的份额。对现有和拟议的评分方法进行广泛的实验性评估表明拟议解决方案的有效性。
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