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.
@article{arxiv.1402.3631,
title = {Privately Solving Linear Programs},
author = {Justin Hsu and Aaron Roth and Tim Roughgarden and Jonathan Ullman},
journal= {arXiv preprint arXiv:1402.3631},
year = {2018}
}