Secure grid‐based density peaks clustering on hybrid cloud for industrial IoT. Issue 2 (2nd October 2020)

• Record Type: Journal Article
• Authors: Sun, Liping; Ci, Shang; Liu, Xiaoqing; Guo, Liangmin; Zheng, Xiaoyao; Luo, Yonglong
• Other Names: Meng Weizhi guestEditor.; Cofta Piotr guestEditor.; Grandison Tyrone W.A. guestEditor.
• Is Part Of: International journal of network management. Volume 31:Issue 2(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-10-02
• Subjects: Computer networks -- Management -- Periodicals; 004.6
• DOI: 10.1002/nem.2139
• Languages: English
• ISSNs: 1055-7148

Abstract

Summary: 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. Abstract : We propose a secure grid‐based density peaks clustering algorithm for a hybrid cloud environment. First, the client uses the homomorphic encryption method to construct an encrypted object with user datasets. Second, the client uploads the encrypted object to the cloud servers to implement the security protocol. Finally, the cloud servers return the clustering result with the disturbance to the client to eliminate the disturbance. Therefore, the secure clustering scheme is applicable to industrial IoT big data and has high security and scalability.