Abstract
Industrial Internet technology has developed rapidly, and the security of industrial data has received much attention. At present, industrial enterprises lack a safe and professional data security system. Thus, industries urgently need a complete and effective data protection scheme. This study develops a three-layer framework with local/fog/cloud storage for protecting sensitive industrial data and defines a threat model. For real-time sensitive industrial data, we use the improved local differential privacy algorithm M-RAPPOR to perturb sensitive information. We encode the desensitized data using Reed–Solomon (RS) encoding and then store them in local equipment to realize low cost, high efficiency, and intelligent data protection. For non-real-time sensitive industrial data, we adopt a cloud-fog collaborative storage scheme based on AES-RS encoding to invisibly provide multilayer protection. We adopt the optimal solution of distributed storage in local equipment and the cloud-fog collaborative storage scheme in fog nodes and cloud nodes to alleviate the storage pressure on local equipment and to improve security and recoverability. According to the defined threat model, we conduct a security analysis and prove that the proposed scheme can provide stronger data protection for sensitive data. Compared with traditional methods, this approach strengthens the protection of sensitive information and ensures real-time continuity of open data sharing. Finally, the feasibility of our scheme is validated through experimental evaluation.
Highlights
Intelligent manufacturing, which consists of a man-machine integrated intelligent system with intelligent machines and human experts, is an inevitable trend in the continuing development of the global manufacturing industry [1]
For non-real-time processing of industrial data, we designed a data protection scheme for cloud-fog collaborative storage based on Advanced Encryption Standard (AES) encryption and RS encoding
We proposed a three-layer protection framework with local/fog/cloud storage for sensitive industrial data and defined a corresponding threat model
Summary
Intelligent manufacturing, which consists of a man-machine integrated intelligent system with intelligent machines and human experts, is an inevitable trend in the continuing development of the global manufacturing industry [1]. With the rapid development of Industrial Internet technology, industrial data protection has attracted much research interest [2]. In the industrial production process, a large amount of sensitive data is generated, including data from the manufacturing process of the production line, product cost information, operations data, operations information, marketing strategy, intellectual property rights, and customer data. If this sensitive data is leaked, it may lead to significant business information loss or even affect the reputation of an enterprise. UpGuard (Sydney, Australia), a cybersecurity company, reported that researchers found more than 540 million records on Amazon’s S3 server, including Facebook user information such as comments, responses, and account names [3]. erefore, in the context of the Industrial Internet, it is a major challenge to ensure that sensitive information is not leaked
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