Gönül Aycı Has Defended Her PhD Thesis: Towards Trustworthy Personal Assistants For Privacy

Title: Towards Trustworthy Personal Assistants For Privacy

Advisors: Arzucan Özgür, Pınar Yolum

AbstractMany 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. 

Our second privacy assistant PEAK is capable of generating such explanations for its recommendations, using latent topics and identified explanation categories to do so. A user study shows users find PEAK's explanations useful and easy to understand. Additionally, privacy assistants can use the explanations to improve their own decision-making, as illustrated by incorporating PEAK into PURE, which results in less uncertain images delegated to the user whilst model performance is not compromised. Overall, our work makes an important contribution towards the development of trustworthy personal assistants capable of preserving users' privacy.

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