The Performance Evaluation of Various Security Techniques in Data Aggregation Model for Big Data
K. Meenakshisundaram1, M. Menaka2
1K. Meenakshisundaram, Associate Professor, Department of CS, Erode Arts and Science College, Erode (Tamil Nadu), India.
2M. Menaka, Research Scholar, Department of CS, Erode Arts and Science College, Erode (Tamil Nadu), India.
Manuscript received on 24 April 2019 | Revised Manuscript received on 03 May 2019 | Manuscript Published on 07 May 2019 | PP: 223-227 | Volume-7 Issue-6S3 April 2019 | Retrieval Number: F1045376S19/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Big Data is defined as a collection of huge size of data sets with different types. It brings some problems like privacy preserving problem and security risk. Event-Role-Attribute based Access Control mechanism was used to ensure the end to end security. It created a flexible boundary with the consideration of event, role and attribute to access the data. Then the data were encrypted by Anonymous Multi-Hop Identity Based Conditional Proxy Re-Encryption (AMH-IBCPRE) where a ciphertext was conditionally and securely shared multiple times without disclosing the identity information of ciphertext senders or recipients and knowledge of underlying message. The problem of high computational complexity and high storage capacity due to the duplicate cipher data was sorted out by a de-duplication technique called Verfiable Hash Convergent Group Signcryption (VHCGS). In this paper, Enhanced Conditional Proxy Re-encryption with Data Aggregation and Masking (ECPR-DAM) and Enhanced Conditional Proxy Re-encryption with Verifiable hash convergent group Signcryption, Data Aggregation and Masking (ECPRVS-DAM) are proposed for data aggregation and data security. The data aggregation model aggregate the data which is the response to maintain the ever improving demands of Big Data. In the process of data aggregation, Fibonacci search is used instead of dichotomic search. Because the Fibonacci search is reducing the average time required to access a location of data. Moreover, a watermarking construction is introduced to enhance the security of Big Data. The watermarking construction provides synchronization marks in the aggregated data and helps protect the data itself at the end points. It improves the big data security. The experimental results prove the effectiveness of the proposed ECPR-DAM and ECPRVS-DAM methods over existing method in terms of storage cost, retrieval time and search time.
Keywords: Big Data; Data Aggregation; Fibonacci Search; Watermarking Construction.
Scope of the Article: Big Data Networking