Title: Towards Trustworthy Personal Assistants For Privacy
Advisors: Arzucan Özgür, Pınar Yolum
Abstract: Many software systems, such as online social networks, enable their users to share information about themselves online. However, users worry about the privacy implications of sharing content. It's a tedious process to make these decisions and it makes managing privacy difficult. Recent approaches to help users manage their privacy involve building personal assistants that can recommend whether a user's content is private or not. However, privacy's ambiguous nature and difficulties explaining assistants' decision-making are challenges hampering users' trust in these systems and therefore also widespread user adoption. In this dissertation, we design trustworthy privacy assistants which can help tackle both challenges. We first propose a personal assistant called PURE that helps its user to make privacy decisions. An important characteristic of PURE is its ability to model uncertainty in its decisions explicitly. When uncertainty is high, no prediction is made and the decision is delegated to the user. By factoring in user's own understanding of privacy, PURE is able to personalize its recommendations. A second crucial factor in fostering trust in personal assistants is their ability to explain their decision-making processes.
