Privacy-Preserving Algorithm For The Cross-Organizational Collaborative Optimization Decisions

In a globalizing world, with the continuous advancement of information technology and intensification of market competition, the competition between modern corporations has evolved into that among Supply Chains. Supply chain management refers to three aspects: information flow, material flow and capital flow, among which, the information flow is the core coordinator and controller in the performance of supply chain. Rapid and smooth information flow among supply chain, i.e, efficient information sharing, can mitigate Bull Effect, and improve the harmony and overall benefits of the supply chain. Although Sharing enterprises’ sensitive information among supply chain enterprises can bring benefits to cooperative enterprises, due to supply chain enterprises are competitors essentially, which restricts sensitive information sharing. Information sharing may (or have to) cause leakage of confidential information of enterprises and bring the negative influence to them. Therefore, enterprises seldom share information with other members in actual production activities. So it is difficult to reach the global optimal objectives of the entire supply chain. There is important significance in studying how to reach global objectives in collaborative optimization decision making without disclosing the participates’privacy information. And how to achieve the global optimization in case of no privacy information leakage is also a hard problem in supply chain management.Secure Multi-party Computation is an important tool to solve this kind of problems. Secure multi-party computation mainly solves the collaborative computing problems between parties who do not trust each other. Secure Multi-party Computation can guarantee parties’ independence of their input and correctness of their computing results with no privacy information leakage. In this paper, on the basis of secure multi-party computation theory as well as the basic protocol of secure multi-party computation, following work have been done:(1) As foundations of this paper, the related theories and research achievements in supply chain information sharing, Secure Multi-Party Computation, distributed optimization and privacy-preserving collaborative optimization are reviewed, and the significance of this study is also discussed;(2) According to a large number of practical applications, this study is not blind to pursue zero leakage of information. For practical use, in the guarantee of safety as well as the efficiency of the protocol increase, we design a set of efficient and practical protocols;(3) For the Linear Programming model on horizontal distribution data, we proposed two solutions:based on random matrix conversion multi-party algorithm and Anti-inference secure two-party algorithm;(4) In the semi-honest model for the Linear Programming model on vertical distribution data, we design. Meanwhile, The correctness, security and complexity have been analyzed and proved;(5) For the Linear Programming model on arbitrary distribution data, a set of Secure Multi-party Computation protocols based on the Karmarkar method is proposed in semi-honest model. Meanwhile, The correctness, security and complexity have been analyzed and proved.