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

Frederic Raber (1) , Felix Kosmalla (1) , Antonio Krueger (1)

1 DFKI - Deutsches Forschungszentrum für Künstliche Intelligenz GmbH = German Research Center for Artificial Intelligence

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.

Domaines: Informatique [cs]

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⟩