Fine-Grained Privacy Setting Prediction Using a Privacy Attitude Questionnaire and Machine Learning
Fine-Grained Privacy Setting Prediction Using a Privacy Attitude Questionnaire and Machine Learning
复制标题
使用隐私态度问卷和机器学习进行细粒度隐私设置预测
DOI：
10.1007/978-3-319-68059-0_48
复制
发表时间：
2017
影响因子：
4.4
通讯作者：
A. Krüger
中科院分区：
文献类型：
--
作者：
Frederic Raber;Felix Kosmalla;A. Krüger
本文建议根据帖子的主题向社交网络(SNS)用户推荐隐私设置。基于对专门设计的问卷的回答，利用机器学习来告知用户隐私模型。然后，该模型为每个帖子提供了应向哪些其他SN用户组披露所述帖子的单独推荐。我们进行了一项前期研究，以找出哪些朋友群通常存在，哪些话题会被讨论。我们解释了机器学习方法的概念，并在验证研究中证明了生成的隐私推荐是准确的，并且被SN用户认为是高度可信的。
This paper proposes to recommend privacy settings to users of social networks (SNs) depending on the topic of the post. Based on the answers to a specifically designed questionnaire, machine learning is utilized to inform a user privacy model. The model then provides, for each post, an individual recommendation to which groups of other SN users the post in question should be disclosed. We conducted a pre-study to find out which friend groups typically exist and which topics are discussed. We explain the concept of the machine learning approach, and demonstrate in a validation study that the generated privacy recommendations are precise and perceived as highly plausible by SN users.