Secured Big Data Based on Ocular Recognition
R. Vanithamani1, T. Kumanan2
1R. Vanithamani, Research Scholar/Computer Science, MAHER University
2Dr. T. Kumanan, Professor/Computer Science, MAHER University
Manuscript received on 05 March 2019 | Revised Manuscript received on 13 March 2019 | Manuscript published on 30 July 2019 | PP: 478-481 | Volume-8 Issue-2, July 2019 | Retrieval Number: B1535078219/19©BEIESP | DOI: 10.35940/ijrte.B1535.078219
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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: In the last few decades, technology has played a main role in developing the electronic devices sharing data, such as sensors, actuators, individual archives, cloud and social networks. Managing this variety of large data called as big data efficiently is a challenging task. The critical challenge in handling big data is security and privacy. At any stage privacy may not be disclosed. Various existing techniques based on encryption and anonymization for security is not perfectly suitable for the unstructured, high speed, large volume big data. In this paper, analysis and discussion is done on how biometric verification and authentication secures big data. Fingerprint being the well-known biometric, ocular recognition is the most reliable biometric technique compared to iris recognition. The unique feature of human iris is its pattern and color which is identified by the type and amount of the pigment in it. The proposed method combines both iris and retinal authentication technique to provide better security for the big data in the emerging field of Internet of Things (IOT).
Keywords: Big Data, Anonymization, Ocular Recognition, Iris Recognition, Biometric Authentication, Mobile Security, Digital Verification.
Scope of the Article: Big Data Networking