Privacy-Preserving Tensor-Based Multiple Clusterings on Cloud for Industrial IoT

Abstract

Aiming at discovering hidden different data structures in big data from different perspectives, a tensor-based multiple clustering method has been developed recently, which can be widely used in Industrial Internet of Things (IoT) to improve production and service quality. However, due to the high computational cost and huge volume of data, outsourcing computing to relatively inexpensive cloud servers can greatly save local costs, but there is a high risk of revealing user privacy. To address the above problem, a privacy-preserving tensor-based multiple clustering method on the secure hybrid cloud is proposed. The proposed scheme utilizes a homomorphic cryptosystem to encrypt object tensors and, then, employs cloud servers to completely implement multiple clustering calculation over encrypted object tensors. Furthermore, a series of related security subprotocols are proposed to support privacy-preserving tensor-based multiple clusterings. In the proposed scheme, only encryption and removing perturbation are performed on the client, which is very lightweight for users. Experimental results show that the proposed scheme is accurate and efficient when clustering objects to different groups, while no private or additional information is leaked. Moreover, when employing more cloud nodes, the scheme has high scalability; thus, it is very suitable for clustering Industrial IoT big data.