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A privacy‐preserving density peak clustering algorithm in cloud computing
Author(s) -
Sun Liping,
Ci Shang,
Liu Xiaoqing,
Zheng Xiaoyao,
Yu Qingying,
Luo Yonglong
Publication year - 2020
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5641
Subject(s) - collusion , cluster analysis , cloud computing , computer science , computer security , private information retrieval , cluster (spacecraft) , personally identifiable information , data mining , information sensitivity , information privacy , service provider , service (business) , algorithm , computer network , artificial intelligence , business , operating system , marketing , industrial organization
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.