  As social robots become increasingly prevalent in day-to-day environments,
they will participate in conversations and appropriately manage the information
shared with them. However, little is known about how robots might appropriately
discern the sensitivity of information, which has major implications for
human-robot trust. As a first step to address a part of this issue, we designed
a privacy controller, CONFIDANT, for conversational social robots, capable of
using contextual metadata (e.g., sentiment, relationships, topic) from
conversations to model privacy boundaries. Afterwards, we conducted two
crowdsourced user studies. The first study (n=174) focused on whether a variety
of human-human interaction scenarios were perceived as either private/sensitive
or non-private/non-sensitive. The findings from our first study were used to
generate association rules. Our second study (n=95) evaluated the effectiveness
and accuracy of the privacy controller in human-robot interaction scenarios by
comparing a robot that used our privacy controller against a baseline robot
with no privacy controls. Our results demonstrate that the robot with the
privacy controller outperforms the robot without the privacy controller in
privacy-awareness, trustworthiness, and social-awareness. We conclude that the
integration of privacy controllers in authentic human-robot conversations can
allow for more trustworthy robots. This initial privacy controller will serve
as a foundation for more complex solutions.
