An Efficient Multi-Mode Three Phase Biometric Data Security Framework For Cloud Computing-Based Servers
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2020 by IJETT Journal|
|Year of Publication : 2020|
|Authors : Ayesha Tarannum, Md. Zia Ur Rahman, Srinivasulu T
|DOI : 10.14445/22315381/IJETT-V68I9P203|
MLA Style: Ayesha Tarannum, Md. Zia Ur Rahman, Srinivasulu T "An Efficient Multi-Mode Three Phase Biometric Data Security Framework For Cloud Computing-Based Servers" International Journal of Engineering Trends and Technology 68.9(2020):10-17.
APA Style:Ayesha Tarannum, Md. Zia Ur Rahman, Srinivasulu T. An Efficient Multi-Mode Three Phase Biometric Data Security Framework For Cloud Computing-Based Servers International Journal of Engineering Trends and Technology, 68(9),10-17.
In public and private cloud servers, multimedia data is increasing day by day and it is hard to provide security properly because of limited storage capacity problems. So, to avoid these storage and security problems, conventional single biometric and various biometric model are used. These conventional models are depending on data size and file format with existed integrity and confidential methods on limited cloud data types. Therefore, to overcome these problems, integrity based a three stage multi use multi modal (MUMM) secured frame work along with large cloud data types is proposed. In the proposed work, to execute a strong data security system on cloud databases biometric images like IRIS and finger knuckle features are utilized. For improving large data security, classification-based CNN, hybrid feature extraction measures, integrity and encryption-based methods are used. Then results shows that proposed model has preferable efficiency compared to conventional multi modular security models for large data cloud data files and it has accomplished positive rate of 0.987, integrity bit variation of 8.7% and better runtime compared existed models.
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Biometric authentication, cloud computing, cyber physical system, convolutional neural network, multi-mode security.