期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2018
卷号：96
期号：12
出版社：Journal of Theoretical and Applied
摘要：Data Mining has gained attention nowadays in the field of sales, marketing, insurance and healthcare applications to name a few. Organizations aspire to perform mining operations on their joint datasets for gaining trade benefits while hiding own sensitive information. Owing to huge resource consumption and less computational power, they often prefer to outsource their data on the cloud platform for entire computation. As there is a risk of exposing the organization's sensitive data from various mistrusted parties involves in it, privacy becomes one of the major challenging issues in cloud computing. Authors have proposed an algorithm were cloud server applies k means clustering on encrypted data sets. A Trusted Party is assumed for key distribution and management. Computations between each party are either performed mutually or via Trusted Authority which involving exchange of sensitive data transfer of each participating parties. Complexity of the algorithm has been analyzed and compared with the existing approach and found that it is linearly depends upon various parameters settings and hence is a better approach while maintaining authenticity and data confidentiality between various participating parties during the mining process.
关键词：Privacy Preserving Data Mining; Pailler Homomorphic Encryption; K-Means Clustering; Cloud Platrorm; Use Case Diagram.
