K-Means Clustering-based Privacy Preserving in Cloud Computing

Abstract

A common data processing technique is clustering, which aims to divide information into related classes. Protecting database privacy is especially important when data is collected from various sensors. Scholars are working to limit the disclosure of personal data related to cloud computing because of significant elements that affect compliance with cloud information security. A k-means clustering approach is proposed in this research to safeguard privacy. To preserve user privacy in the online storage context, the clustering process does not reveal personal information or leak the variation matrix. Our security k-means primarily consist of a confidentiality cluster process computation. Two privacy-preserving techniques for clustering media computing are proposed in this research. Java is implementing our calculation method. Our version of the confidentiality method of clustering has been thoroughly tested on huge data sets. Results from experiments and theories confirm the security and accuracy of our process.