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arXiv · 1402.3631

Privately Solving Linear Programs

Justin Hsu·Aaron Roth·Tim Roughgarden·Jonathan Ullman
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Abstract

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.

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Justin Hsu, Aaron Roth, Tim Roughgarden, Jonathan Ullman. 2014-02-15. Privately Solving Linear Programs. https://doi.org/10.1007/978-3-662-43948-7_51

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