  Technology advances in areas such as sensors, IoT, and robotics, enable new
collaborative applications (e.g., autonomous devices). A primary requirement
for such collaborations is to have a secure system which enables information
sharing and information flow protection. Policy-based management system is a
key mechanism for secure selective sharing of protected resources. However,
policies in each party of such a collaborative environment cannot be static as
they have to adapt to different contexts and situations. One advantage of
collaborative applications is that each party in the collaboration can take
advantage of knowledge of the other parties for learning or enhancing its own
policies. We refer to this learning mechanism as policy transfer. The design of
a policy transfer framework has challenges, including policy conflicts and
privacy issues. Policy conflicts typically arise because of differences in the
obligations of the parties, whereas privacy issues result because of data
sharing constraints for sensitive data. Hence, the policy transfer framework
should be able to tackle such challenges by considering minimal sharing of data
and support policy adaptation to address conflict. In the paper we propose a
framework that aims at addressing such challenges. We introduce a formal
definition of the policy transfer problem for attribute-based policies. We then
introduce the transfer methodology that consists of three sequential steps.
Finally we report experimental results.
