  Many software systems, such as online social networks enable users to share
information about themselves. While the action of sharing is simple, it
requires an elaborate thought process on privacy: what to share, with whom to
share, and for what purposes. Thinking about these for each piece of content to
be shared is tedious. Recent approaches to tackle this problem build personal
assistants that can help users by learning what is private over time and
recommending privacy labels such as private or public to individual content
that a user considers sharing. However, privacy is inherently ambiguous and
highly personal. Existing approaches to recommend privacy decisions do not
address these aspects of privacy sufficiently. Ideally, a personal assistant
should be able to adjust its recommendation based on a given user, considering
that user's privacy understanding. Moreover, the personal assistant should be
able to assess when its recommendation would be uncertain and let the user make
the decision on her own. Accordingly, this paper proposes a personal assistant
that uses evidential deep learning to classify content based on its privacy
label. An important characteristic of the personal assistant is that it can
model its uncertainty in its decisions explicitly, determine that it does not
know the answer, and delegate from making a recommendation when its uncertainty
is high. By factoring in the user's own understanding of privacy, such as risk
factors or own labels, the personal assistant can personalize its
recommendations per user. We evaluate our proposed personal assistant using a
well-known data set. Our results show that our personal assistant can
accurately identify uncertain cases, personalize them to its user's needs, and
thus helps users preserve their privacy well.
