Uncertainty-aware Personal Assistant and Explanation Method for Privacy Decisions

Gönül Aycı (Bogazici University)

Abstract

In many of today's software systems, most notably online social networks, users can share personal information. Behind the simple action of sharing is a more complicated thought process regarding privacy: which content to share, with whom to share, and why to share. For a user, it's time-consuming and error-prone to check individual personal content for privacy violations. Hence, it would be ideal if a personal assistant can learn its users' privacy preferences and subsequently help users' decision-making by signaling potentially private content. A personalized privacy assistant can help its user make privacy decisions taking into account the ambiguity and uncertainty of privacy predictions as well as its user's personal preferences. Moreover, an explanation of why an image is considered public or private can aid the user in understanding the assistant's decisions.