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Many software systems, such as online social networks enable users to shareinformation about themselves. While the action of sharing is simple, itrequires an elaborate thought process on privacy: what to share, with whom toshare, and for what purposes. Thinking about these for each piece of content tobe shared is tedious. Recent approaches to tackle this problem build personalassistants that can help users by learning what is private over time andrecommending privacy labels such as private or public to individual contentthat a user considers sharing. However, privacy is inherently ambiguous andhighly personal. Existing approaches to recommend privacy decisions do notaddress these aspects of privacy sufficiently. Ideally, a personal assistantshould be able to adjust its recommendation based on a given user, consideringthat user's privacy understanding. Moreover, the personal assistant should beable to assess when its recommendation would be uncertain and let the user makethe decision on her own. Accordingly, this paper proposes a personal assistantthat uses evidential deep learning to classify content based on its privacylabel. An important characteristic of the personal assistant is that it canmodel its uncertainty in its decisions explicitly, determine that it does notknow the answer, and delegate from making a recommendation when its uncertaintyis high. By factoring in the user's own understanding of privacy, such as riskfactors or own labels, the personal assistant can personalize itsrecommendations per user. We evaluate our proposed personal assistant using awell-known data set. Our results show that our personal assistant canaccurately identify uncertain cases, personalize them to its user's needs, andthus helps users preserve their privacy well.

Uncertainty-aware Personal Assistant for Making Personalized Privacy Decisions