Privacy preserving mechanisms for optimizing cross-organizational collaborative decisions based on the Karmarkar algorithm
作者：
Highlights：
• Propose an encryption protocol by integrating Secure Multi-party Computation (SMC) for sharing and calculating private information in the context of cross-organizational collaboration.
• Introduce the revised Karmarkar algorithm to deal with privacy-preserving distributed linear programming (LP) for multi-party computation and two-party computation in the scenario of mutual distrust and semi-honest participants.
• Demonstrate the effectiveness and efficiency that can be achieved in the revised Karmarkar algorithm when it is applied in SMC.
摘要
•Propose an encryption protocol by integrating Secure Multi-party Computation (SMC) for sharing and calculating private information in the context of cross-organizational collaboration.•Introduce the revised Karmarkar algorithm to deal with privacy-preserving distributed linear programming (LP) for multi-party computation and two-party computation in the scenario of mutual distrust and semi-honest participants.•Demonstrate the effectiveness and efficiency that can be achieved in the revised Karmarkar algorithm when it is applied in SMC.
论文关键词：Collaborative optimization,Privacy preserving mechanisms,The Karmarkar algorithm,Secure Multi-Party Computation (SMC),Secure Two-Party Computation (STC)
论文评审过程：Received 2 February 2016, Revised 9 August 2017, Accepted 18 October 2017, Available online 2 November 2017, Version of Record 2 November 2017.
论文官网地址：https://doi.org/10.1016/j.is.2017.10.008
