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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Privately Solving Linear Programs
verfasst von : Justin Hsu, Aaron Roth, Tim Roughgarden, Jonathan Ullman
Erschienen in: Automata, Languages, and Programming
Verlag: Springer Berlin Heidelberg
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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.