  Existing question-answering research focuses on unanswerable questions in the
context of always providing an answer when a system can\dots but what about
cases where a system {\bf should not} answer a question. This can either be to
protect sensitive users or sensitive information. Many models expose sensitive
information under interrogation by an adversarial user. We seek to determine if
it is possible to teach a question-answering system to keep a specific fact
secret. We design and implement a proof-of-concept architecture and through our
evaluation determine that while possible, there are numerous directions for
future research to reduce system paranoia (false positives), information
leakage (false negatives) and extend the implementation of the work to more
complex problems with preserving secrecy in the presence of information
aggregation.
