Protection of Sensitive Data in Industrial Internet Based on Three-Layer Local/Fog/Cloud Storage

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.

A Survey on Industrial Internet of Things Security: Requirements, Attacks, AI-Based Solutions, and Edge Computing Opportunities

“…Advantages Limitations [190] Hybrid AES-RSA Confidentiality, secure data sharing It efficiently protects the secrecy of the data and enables devices to recover the data in a secure manner It relies on RSA (i.e., an asymmetric encryption method), which is slow [191] Hierarchical and distributed Secure data at rest, while providing IIoT devices with status updates…”

“…Liu et al [190] introduced a framework to preserve data storage security utilizing a privacy algorithm known as local differential and a combined AES-RSA encryption technique. The authors adopted the encryption technique to jointly and efficiently protect the secrecy of the data while making it possible to recover the data in a secure manner (i.e., an entity with the appropriate key can recover the data).…”