  Privacy and ethics of citizens are at the core of the concerns raised by our
increasingly digital society. Profiling users is standard practice for software
applications triggering the need for users, also enforced by laws, to properly
manage privacy settings. Users need to manage software privacy settings
properly to protect personally identifiable information and express personal
ethical preferences. AI technologies that empower users to interact with the
digital world by reflecting their personal ethical preferences can be key
enablers of a trustworthy digital society. We focus on the privacy dimension
and contribute a step in the above direction through an empirical study on an
existing dataset collected from the fitness domain. We find out which set of
questions is appropriate to differentiate users according to their preferences.
The results reveal that a compact set of semantic-driven questions (about
domain-independent privacy preferences) helps distinguish users better than a
complex domain-dependent one. This confirms the study's hypothesis that moral
attitudes are the relevant piece of information to collect. Based on the
outcome, we implement a recommender system to provide users with suitable
recommendations related to privacy choices. We then show that the proposed
recommender system provides relevant settings to users, obtaining high
accuracy.
