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Abstract Protecting privacy and ethics of citizens is among the core concerns raised by an increasingly digital society. Profiling users is common practice for software applications triggering the need for users, also enforced by laws, to manage privacy settings properly. Users need to properly manage these settings to protect personally identifiable information and express personal ethical preferences. This has shown to be very difficult for several concurrent reasons. However, profiling technologies can also empower users in their interaction with the digital world by reflecting personal ethical preferences and allowing for automatizing/assisting users in privacy settings. In this way, if properly reflecting users’ preferences, privacy profiling can become a key enabler for a trustworthy digital society. We focus on characterizing/collecting users’ privacy preferences and contribute a step in this direction through an empirical study on an existing dataset collected from the fitness domain. We aim to understand which set of questions is more appropriate to differentiate users according to their privacy 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. 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.
Ruscio et al. (Sun,) studied this question.