Application of secure multi-party computation in linear programming

Fu Zu-feng, Wang Hai-ying, Wu Yong-wu
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Abstract

The existing solution to the privacy preserving linear programming, can leak the user's private data when the data is much less. In this paper, the secure multiparty computation is generalized to the problem of privacy-preserving linear programming, and we present a computing protocol of privacy-preserving linear programming. The protocol is applied to consider the problem of linear programming with less and vertically distributed data, not only the maximum value of the original linear programming can be calculated in the case having optimal solution, but also the private data of all participants can be protected in the calculation.
安全多方计算在线性规划中的应用
现有的隐私保护线性规划解决方案,在用户的隐私数据非常少的情况下,可能会泄露用户的隐私数据。将安全多方计算推广到保护隐私的线性规划问题,给出了一个保护隐私线性规划的计算协议。将该协议应用于考虑数据量少且垂直分布的线性规划问题,不仅可以在具有最优解的情况下计算出原线性规划的最大值,而且可以在计算中保护所有参与者的私有数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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