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Deriving Privacy Settings for Location Sharing: Are Context Factors Always the Best Choice?

机译:导出位置共享的隐私设置:上下文因素是否始终是最佳选择?

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Research has observed context factors like occasion and time as influential factors for predicting whether or not to share a location with online friends. In other domains like social networks, personality was also found to play an important role. Furthermore, users are seeking a fine-grained disclosement policy that also allows them to display an obfuscated location, like the center of the current city, to some of their friends. In this paper, we observe which context factors and personality measures can be used to predict the correct privacy level out of seven privacy levels, which include obfuscation levels like center of the street or current city. Our results show that a prediction is possible with a precision 20% better than a constant value. We will give design indications to determine which context factors should be recorded, and how much the precision can be increased if personality and privacy measures are recorded using either a questionnaire or automated text analysis.
机译:研究已经观察到诸如场合和时间之类的情境因素作为预测是否与在线朋友共享位置的影响因素。在社交网络等其他领域,人格也起着重要作用。此外,用户正在寻求一种精细的公开政策,该政策还允许他们向一些朋友显示混淆的位置,例如当前城市的中心。在本文中,我们观察了哪些环境因素和人格测度可用于预测七个隐私级别中的正确隐私级别,其中包括诸如街道中心或当前城市之类的混淆级别。我们的结果表明,可以以比常数值高20%的精度进行预测。我们将给出设计指示,以确定应该记录哪些上下文因素,以及如果使用问卷或自动文本分析记录个性和隐私措施,则可以提高精度。

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