Privacy Support for the Total Learning Architecture: Summit Report
2018; Knijnenburg, B.P.; et al
How can we reconcile the need for extensive customizability with users' apparent lack of skills and motivation to manage their own privacy settings? In this report we investigate User-Tailored Privacy as means to support users' privacy decision-making. With User-Tailored Privacy (UTP), a system would first measure users' privacy-related characteristics and behaviors, use this as input to model their privacy preferences, and then adapt the system's privacy settings to these preferences (Figure 1).This adaptation could take the form of a default setting or a recommendation, either with or without an accompanying justification.