An Efficient Data Segmentation and Replication Technique for Cloud using Fuzzy Centrality Measures
S. Periyanatchi1, K. Chitra2

1S. Periyanatchi, Research Scholar, Bharathiyar University, Coimbatore (Tamil Nadu), India.
2Dr. K. Chitra, Assistant Professor, Department of CSE, Govt Arts College, Melur (Tamil Nadu), India.
Manuscript received on 19 July 2019 | Revised Manuscript received on 03 August 2019 | Manuscript Published on 10 August 2019 | PP: 624-628 | Volume-8 Issue-2S3 July 2019 | Retrieval Number: B11150782S319/2019©BEIESP | DOI: 10.35940/ijrte.B1115.0782S319
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Abstract: Cloud computing is a creating worldview to give reliable and resilient infrastructure permitting the clients (data proprietors) to store their data and the data purchasers (clients) can get to the data from cloud servers. This worldview decreases storage and maintenance cost of the data proprietor. Notwithstanding, cloud data storage still offers ascend to security related issues. In the event of shared data, the data face both cloud-explicit and insider threats. In this work, we propose fuzzy centrality measure based division and replication of data in the cloud for perfect execution and security that consider both security and execution issues. In our framework, we separate a data records and imitate the isolated data over the cloud center points utilizing fuzzy centrality measures. Every one of the nodes stores just a solitary data fragment of a particular data document that guarantees that even if there should arise an occurrence of a fruitful attack, no significant information is uncovered to the attacker. In addition, the cloud nodes storing the data fragments, are separated with certain distance by methods for altered fuzzy T-coloring to prohibit an attacker of predicting the locations of the fragments. We likewise contrast the exhibition of the our methodology and other standard replication plans. The greater amount of security with improved performance was observed.
Keywords: Cloud Fuzzy Data Framework Methods.
Scope of the Article: Fuzzy Logics