Recommender systems are widely used to help deal with the
problem of information overload. However, recommenders raise serious
privacy and security issues. The personal information collected by recommenders
raises the risk of unwanted exposure of that information. Also,
malicious users can bias or sabotage the recommendations that are provided
to other users. This paper raises important research questions in
three topics relating to exposure and bias in recommender systems: the
value and risks of the preference information shared with a recommender,
the effectiveness of shilling attacks designed to bias a recommender, and
the issues involved in distributed or peer-to-peer recommenders. The goal
of the paper is to bring these questions to the attention of the information
and communication security community, to invite their expertise in
addressing them.