Design and analysis of an optimal ECC algorithm with effective access control mechanism for big data

• Published in: Multimedia tools and applications Vol. 79; no. 15-16; pp. 9757 - 9783
• Main Authors: Verma, Om Prakash, Jain, Nitin, Pal, Saibal Kumar
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2020; Springer Nature B.V
• Subjects: Access control; Algorithms; Big Data; Clustering; Computer Communication Networks; Computer Science; Cryptography; Curves; Data management; Data storage; Data Structures and Information Theory; Multimedia Information Systems; Optimization; Special Purpose and Application-Based Systems; Grasshopper optimization; Access control mechanism; Elliptic curve cryptography; Fuzzy c means; Big data; Map reduce
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-019-7677-2

Big data is a high volume data, as it comprises complex and large volume of information. A successful solution is to redistribute the data to a cloud server that has the capacity of storing and processing big data in an effective manner. The main intention of the research is to secure storage of big data and effective access control mechanism. The main stages of the proposed method are map reduce framework, secure storage process and access control mechanism process. Map Reduce is a distributed programming framework used to process big data. In mapper, the input dataset is grouped using hybrid kernel fuzzy c means (HKFCM) clustering algorithm. Finally, the reduced output is fed to the data owner for secure storage. In secure storage process, the suggested method utilizes optimal elliptic curve cryptography (OECC). Here the fundamental values are optimally selected by Modified grasshopper optimization algorithm (MGOA). In the access control mechanism, the effective policy update is proposed along with data storage construction and data deconstruction stage. The routine of the recommended method is assessed using memory and execution time by differentiating the number data size, number cluster size and the number of mapper. The proposed method attains the minimum time and memory utilization when compared to the existing method. The suggested method is implemented in cloud sim with Hadoop Map-reduce framework.