Fine-Grained Privacy Setting Prediction Using a Privacy Attitude Questionnaire and Machine Learning

Abstract : 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.
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Frederic Raber, Felix Kosmalla, Antonio Krueger. Fine-Grained Privacy Setting Prediction Using a Privacy Attitude Questionnaire and Machine Learning. 16th IFIP Conference on Human-Computer Interaction (INTERACT), Sep 2017, Bombay, India. pp.445-449, ⟨10.1007/978-3-319-68059-0_48⟩. ⟨hal-01679834⟩

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