Privately Solving Linear Programs
  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. 
Justin Hsu and Aaron Roth and Tim Roughgarden and Jonathan Ullman
cs.DS cs.CR cs.LG