  In this paper, we initiate the systematic study of solving linear programs
under differential privacy. The first step is simply to define the problem: to
this end, we introduce several natural classes of private linear programs that
capture different ways sensitive data can be incorporated into a linear
program. For each class of linear programs we give an efficient, differentially
private solver based on the multiplicative weights framework, or we give an
impossibility result.
