Publication
Fine-Grained Privacy Setting Prediction Using a Privacy Attitude Questionnaire and Machine Learning
Frederic Raber; Felix Kosmalla; Schneeberger Tanja; 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, Pages 445-449, ISBN 978-3-319-68059-0, Springer International Publishing, Cham, 2017.
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.