A privacy‐preserving density peak clustering algorithm in cloud computing. (8th January 2020)

• Record Type: Journal Article
• Authors: Sun, Liping; Ci, Shang; Liu, Xiaoqing; Zheng, Xiaoyao; Yu, Qingying; Luo, Yonglong
• Is Part Of: Concurrency and computation. Volume 32:Number 11(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-01-08
• Subjects: cloud computing -- data mining -- density peak clustering -- homomorphic encryption -- privacy preservation; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5641
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Aiming at preventing the privacy disclosure of sensitive information, issues related to privacy protection in cloud computing have attracted the interest of researchers. To protect the privacy of users during clustering in a cloud computing environment, we present a privacy‐preserving density peak clustering (PPDPC) algorithm that neither discloses personal privacy information nor leaks the cluster centers. Our scheme contains two steps of density peak clustering: First, a cloud service provider calculates the cluster centers without knowing each participant's private data and without disclosing any cluster center information to the other participants, and second, participant allocation is secure and every participant is prevented from identifying the other members of the same cluster. Security analysis and comparison experiments show that the proposed PPDPC algorithm not only obtains good accuracy with respect to density peak clustering but also resists collusion attacks even if the cloud service provider is collaborating with all except one participant. Both theoretical analysis and experimental results confirm the security and accuracy of our method.