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. © 2014 Springer-Verlag.
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CITATION STYLE
Hsu, J., Roth, A., Roughgarden, T., & Ullman, J. (2014). Privately solving linear programs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8572 LNCS, pp. 612–624). Springer Verlag. https://doi.org/10.1007/978-3-662-43948-7_51
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