FOC-PH-CP-ABE: An Efficient CP-ABE Scheme With Fully Outsourced Computation and Policy Hidden in the Industrial Internet of Things

The industrial Internet of Things (IIoT) generates a large amount of private data, which can be used to improve production efficiency and optimize production management decisions. However, the openness of the network and the limited resources of sensor devices pose security threats to industrial privacy data, such as illegal access and leakage. Attribute-based encryption (ABE) is a promising solution for solving the problem of private data sharing. Nevertheless, many time-consuming operations, such as bilinear pairing, security of access policies, and attribute revocation issues, pose challenges for ABE in practical applications. To address the above issues, we propose an efficient policy hiding ciphertext-policy ABE (CP-ABE) scheme based on a multivalued attribute access structure with wildcards. It introduces fog computing to achieve fully outsourced computation, reducing the computational overhead of resource constrained terminals. Meanwhile, attribute revocation and user revocation mechanisms were designed to achieve flexible and fine-grained access control. Based on the idea of reduction, we have demonstrated that our scheme is secure under the assumption of the decision q-bilinear Diffie-Hellman exponent. In addition, our scheme has both backward and forward securities. Finally, we compared and analyzed the proposed scheme with the existing schemes in terms of functionality and performance. Theoretical analysis and experimental simulation results show that our scheme has relatively complete functions and has certain advantages in communication costs and computational overhead.

I. Introduction

Industrial Internet of Things (IIoT), as an emerging technology, achieves comprehensive interconnection and intelligent manufacturing by linking smart devices widely deployed in factories [1]. These smart devices in IIoT can efficiently collect and share data through the network, establishing a great collaborative work environment for traditional industrial systems. A typical IIoT system involves various industrial data, from real-time equipment status data, production management data, transportation data, and so on, to employee management data, which can be used to improve production efficiency, reduce costs, optimize resource utilization, provide real-time data analysis and decision support, and ultimately enhance the overall production value of the enterprise [2].