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.
https://hal.inria.fr/hal-01679834 Contributor : Hal IfipConnect in order to contact the contributor Submitted on : Wednesday, January 10, 2018 - 11:32:19 AM Last modification on : Thursday, June 4, 2020 - 6:24:02 PM Long-term archiving on: : Saturday, May 5, 2018 - 6:41:55 AM
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⟩