  Increasingly more attention is paid to the privacy in online applications due
to the widespread data collection for various analysis purposes. Sensitive
information might be mined from the raw data during the analysis, and this led
to a great privacy concern among people (data providers) these days. To deal
with this privacy concerns, multitudes of privacy-preserving computation
schemes are proposed to address various computation problems, and we have found
many of them fall into a class of problems which can be solved by greedy
algorithms.
  In this paper, we propose a framework for distributed greedy algorithms in
which instances in the feasible set come from different parties. By our
framework, most generic distributed greedy algorithms can be converted to a
privacy preserving one which achieves the same result as the original greedy
algorithm while the private information associated with the instances is still
protected.
