Secure grid‐based density peaks clustering on hybrid cloud for industrial IoT

Abstract

Cloud computing gives clients the convenience of outsourcing data calculations. However, it also brings the risk of privacy leakage, and datasets that process industrial IoT information have a high computational cost for clients. To address these problems, this paper proposes a secure grid-based density peaks clustering algorithm for a hybrid cloud environment. First, the client utilizes the homomorphic encryption algorithm to construct encrypted objects with client dataset. Second, the client uploads the encrypted data to the cloud servers to implement our security protocol. Finally, the cloud servers return the clustering results with the disturbance to the client. The experimental results on the UCI datasets and the smart power grid dataset reveal that the secure algorithm presented in this paper can improve upon the precision and efficiency of other clustering algorithms while also preserving user privacy. Moreover, it only performs encryption and removes the disturbance operation on the client, so that the client has lower computational complexity. Therefore, the secure clustering scheme proposed in this paper is applicable to industrial IoT big data and has high security and scalability.

Clustering and parallel indexing of big IoT data in the fog‐cloud computing level

“…Despite the wide use of the k-means algorithm, for its simplicity and efficiency, it is difficult to converge to nonconvex datasets. 55 To solve the problem of the clustering of large data, Woodley et al 56 proposed the K-tree, which is a hierarchical data structure and clustering algorithm. In a first step, parallelism is applied on the multicore system.…”

“…This makes it useful to develop data clustering algorithms in a cloud computing environment. Despite the wide use of the k‐means algorithm, for its simplicity and efficiency, it is difficult to converge to nonconvex datasets 55 . To solve the problem of the clustering of large data, Woodley et al 56 proposed the K‐tree, which is a hierarchical data structure and clustering algorithm.…”