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.
A full version of the paper with the omitted proofs and sections can be found at http://arxiv.org/abs/1402.3631 .
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Hsu, J., Roth, A., Roughgarden, T., Ullman, J. (2014). Privately Solving Linear Programs. In: Esparza, J., Fraigniaud, P., Husfeldt, T., Koutsoupias, E. (eds) Automata, Languages, and Programming. ICALP 2014. Lecture Notes in Computer Science, vol 8572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43948-7_51
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DOI: https://doi.org/10.1007/978-3-662-43948-7_51
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