大数据环境下跨组织间协同优化决策的隐私保护算法

组织协同决策分析的数据具有大数据的分布性、异构性和隐私性等典型特征。安全多方计算是一种基于协同机制或协议的隐私保护算法，但它一些常用的单调张成等方法却无法挣脱计算复杂性的困扰。本文主要研究组织间两种结构的协同优化决策问题，提出针对决策变量与约束参数隐私保护的安全多方计算协议，并给出相对应的安全证明。研究表明对于本文构造的SMC协议，可以降低优化协同决策的计算复杂度，部分隐私信息无须加工传送也可以完成计算任务。

Privacy-preserving Algorithm for Cross-organizational Collaborative Optimization Decisions Based on Big Data

The cross-organizational data has the typical characteristics of big data in collaborative optimi-zation decision-making , such as distributedness , heterogeneity , and privacy etc .Secure multi-party com-putation（SMC） is a privacy-preserving algorithm, based on collaborative mechanisms or protocols .How-ever , the methods typically used , such as monotone span program , cannot get rid of the computational complexity .It discussed two kinds of problems in collaborative optimization decision , and proposed se-cure multi-party computation protocols for the privacy-preserving of constraint parameters and decision variables.Then, it gave security proof.The results show that the SMC protocols can reduce the computa-tional complexity of collaborative optimization decisions , and computation of some private information can be completed without transferred processing .