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

Frederic Raber, Felix Kosmalla, Tanja Schneeberger, Antonio Krüger

In: Regina Bernhaupt, Girish Dalvi, Anirudha Joshi, Devanuj K. Balkrishan, Jacki O'Neill, Marco Winckler (editor). Human-Computer Interaction -- INTERACT 2017: 16th IFIP TC 13 International Conference, Proceedings. IFIP Conference on Human-Computer Interaction (INTERACT-2017) 16th September 25-29 Mumbai India IV Pages 445-449 ISBN 978-3-319-68059-0 Springer International Publishing Cham 2017.


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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German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz