导出位置共享的隐私设置:上下文因素总是最好的选择吗?

Frederic Raber, A. Krüger
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引用次数: 8

摘要

研究发现,场合和时间等环境因素是预测是否与在线朋友分享位置的影响因素。在社交网络等其他领域,个性也起着重要作用。此外,用户正在寻求一种细粒度的披露策略,允许他们向一些朋友显示一个模糊的位置,比如当前城市的中心。在本文中,我们观察了哪些情境因素和人格测量可以用来预测七个隐私级别中正确的隐私级别,其中包括街道中心或当前城市的混淆级别。我们的结果表明,预测精度比恒定值提高20%是可能的。我们将给出设计指示,以确定应该记录哪些上下文因素,以及如果使用问卷调查或自动文本分析记录个性和隐私措施,可以提高多少精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deriving Privacy Settings for Location Sharing: Are Context Factors Always the Best Choice?
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.
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