A privacy-preserving resource trading scheme for Cloud Manufacturing with edge-PLCs in IIoT

Abstract

With the development of industrial Internet of things (IIoT), Cloud Manufacturing has been increasingly popular to the manufacturing industry. It can provide resource-sharing and on-demand manufacturing services as well as automatic collaborative manufacturing with the help of edge Programmable Logic Controllers (edge-PLCs). In such a system, there is a high risk of exposing user privacy and trading secret, due to exposure of sensitive transaction data to public servers. We propose a new privacy-preserving resource-trading scheme (PRTS), which leverages the concept of homomorphic cryptography and asymmetric searchable encryption, to simultaneously protect the privacy of the equipment factory and parts factories, while supporting best matching results in terms of parts parameters and price. Furthermore, a random forest-based method is applied to identify abnormal participants. The experimental results and security analysis show that the proposed scheme is accurate, effective, and secure, even under Off-line Keyword Guessing Attacks. Finally, encrypted data can resist analysis from mainstream machine learning techniques.