A Secure High-Order CFS Algorithm on Clouds for Industrial Internet of Things

To uncover latent data structures in big data, a high-order clustering algorithm by fast search and find of density peaks has emerged recently, and will bring great application values in industrial Internet-of-Things data management and analysis. With the popularity of cloud computing, it provides users with the convenience of outsourcing calculations while bringing the risk of privacy disclosure. Aiming at the problem above, from the characteristics of the secure cloud service system, this paper proposes a secure high-order clustering algorithm by fast search and find of density peaks on hybrid cloud. In the proposed scheme, the client first builds the encrypted object tensors with user data using homomorphic encryption, then uploads them to the cloud to completely implement the proposed protocols. In the end, clustering results perturbed with random numbers are returned to client for remove perturbation. The performance of the proposed method is evaluated on a smart grid dataset in terms of clustering accuracy, efficiency, and speedup ratio. Experimental results reveal that the proposed approach can accurately and effectively cluster big data without disclosing user privacy while ensuring that the client is very lightweight. Therefore, the proposed scheme with high security and scalability is suitable for clustering industrial Internet-of-Things big data.