Publikation

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 (Hrsg.). 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 Seiten 445-449 ISBN 978-3-319-68059-0 Springer International Publishing Cham 2017.

Abstrakt

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.

Weitere Links

paper.pdf (pdf, 262 KB)

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence