  The identity problem today is a data-sharing problem. Today the fixed
attributes approach adopted by the consumer identity management industry
provides only limited information about an individual, and therefore is of
limited value to the service providers and other participants in the identity
ecosystem. This paper proposes the use of the Open Algorithms (OPAL) paradigm
to address the increasing need for individuals and organizations to share data
in a privacy-preserving manner. Instead of exchanging static or fixed
attributes about users, participants in the ecosystem will be able to obtain
better insight through a collective sharing of algorithms, governed through a
trust network. Algorithms for specific data-sets must be vetted to be
privacy-preserving, fair and free from bias.
