Visible to the public A Study of Big Data Security on a Partitional Clustering Algorithm with Perturbation Technique

TitleA Study of Big Data Security on a Partitional Clustering Algorithm with Perturbation Technique
Publication TypeConference Paper
Year of Publication2020
AuthorsMarichamy, V. S., Natarajan, V.
Conference Name2020 International Conference on Smart Electronics and Communication (ICOSEC)
Date PublishedSept. 2020
ISBN Number978-1-7281-5461-9
KeywordsAccuracy, Big Data, big data privacy, big data security, Clustering algorithms, Clustering Tme, Conferences, data privacy, Distributed databases, execution time, F-Socre Measure, Hadoop distributed file system, HDFS, Human Behavior, Metrics, parallel processing, partitional clustering algorithm, Partitioning algorithms, pattern clustering, PCA, Perturbation methods, perturbation technique, Precision, principal component analysis, privacy preserving, pubcrawl, resilience, Resiliency, Scalability, security, security of data

Partitional Clustering Algorithm (PCA) on the Hadoop Distributed File System is to perform big data securities using the Perturbation Technique is the main idea of the proposed work. There are numerous clustering methods available that are used to categorize the information from the big data. PCA discovers the cluster based on the initial partition of the data. In this approach, it is possible to develop a security safeguarding of data that is impoverished to allow the calculations and communication. The performances were analyzed on Health Care database under the studies of various parameters like precision, accuracy, and F-score measure. The outcome of the results is to demonstrate that this method is used to decrease the complication in preserving privacy and better accuracy than that of the existing techniques.

Citation Keymarichamy_study_2020