Journal of Guangdong University of Technology ›› 2016, Vol. 33 ›› Issue (05): 15-21.doi: 10.3969/j.issn.1007-7162.2016.05.004

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Privacy-Preserving Algorithm Research on Horizontally Partitioned Data of Collaborative Optimization Decisions

Liu Zhi-hui, Liu Hong-wei, Zhan Ming-jun, Xiao Qi, Chen Xiao-xuan   

  1. School of Management, Guangdong University of Technology, Guangzhou 510520, China
  • Received:2016-03-09 Online:2016-09-10 Published:2016-09-10

Abstract:

In the cross-organizational collaborative optimal decision-making, the parameters of optimization usually originate from different subject data. It is difficult to solve the global optimization problems due to the lack of a trusted third party. A method combining the random matrix transformation and encryption technology is put forward to solve the collaborative optimization of the horizontally partitioned data. This method also overcomes the potential destabilizing impact of structural problems and structural solutions when using disturbance or difference algorithm. On one hand, the security protocol can ensure the consistency of calculation results with privacy-preserving and centralized results. On the other side, it can prevent the potential inference attack. The study can be widely used in collaborative computing security issues of optimization decision problems among enterprise alliance or supply chain.

Key words: data horizontally partitioned; security protocol; inference attack

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