Secure collaborative supply chain planning and inverse optimization  The JELS model
It is a well-acknowledged fact that collaboration between different members of a supply chain yields a significant potential to increase overall supply chain performance. Sharing private information has been identified as prerequisite for collaboration and, at the same time, as one of its major obstacles. One potential avenue for overcoming this obstacle is Secure Multi-Party Computation (SMC). SMC is a cryptographic technique that enables the computation of any (well-defined) mathematical function by a number of parties without any party having to disclose its input to another party. In this paper, we show how SMC can be successfully employed to enable joint decision-making and benefit sharing in a simple supply chain setting. We develop secure protocols for implementing the well-known Joint Economic Lot Size (JELS) Model with benefit sharing in such a way that none of the parties involved has to disclose any private (cost and capacity) data. Thereupon, we show that although computation of the models outputs can be performed securely, the approach still faces practical limitations. These limitations are caused by the potential of inverse optimization, i.e., a party can infer another partys private data from the output of a collaborative planning scheme even if the computation is performed in a secure fashion. We provide a detailed analysis of inverse optimization potentials and introduce the notion of stochastic security, a novel approach to assess the additional information a party may learn from joint computation and benefit sharing. Based on our definition of stochastic security we propose a stochastic benefit sharing rule, develop a secure protocol for this benefit sharing rule, and assess under which conditions stochastic benefit sharing can guarantee secure collaboration.
collaboration secure multi-party computation supply chain management information sharing