Privacy-preserving linear programming |
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Authors: | O L Mangasarian |
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Institution: | (3) Computer Sci. Dept. Univ. Wisconsin–Eau Claire, Eau Claire, WI 54701, USA |
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Abstract: | We propose a privacy-preserving formulation of a linear program whose constraint matrix is partitioned into groups of columns
where each group of columns and its corresponding cost coefficient vector are owned by a distinct entity. Each entity is unwilling
to share or make public its column group or cost coefficient vector. By employing a random matrix transformation we construct
a linear program based on the privately held data without revealing that data or making it public. The privacy-preserving
transformed linear program has the same minimum value as the original linear program. Component groups of the solution of
the transformed problem can be decoded and made public only by the original group that owns the corresponding columns of the
constraint matrix and can be combined to give an exact solution vector of the original linear program. |
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Keywords: | |
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