%0 Journal Article
%@ 21945357
%A Bhattacharjee, S.
%A Chakkaravarhty, D.M.
%A Chakkaravarty, M.
%A Rahim, L.B.A.
%A Ramadhani, A.W.
%D 2021
%F scholars:16017
%I Springer
%J Advances in Intelligent Systems and Computing
%K Big data; Computer graphics; Computer graphics equipment; Data privacy; Errors; Geometry; Graphics processing unit; Information management; Program processors; Public key cryptography, Avalanche effects; Efficient managements; Elliptic curve cryptography; Error control techniques; Integrity attacks; Memory management; Parallel executions; Transmission systems, Memory management units
%P 263-280
%R 10.1007/978-981-15-5616-6₁₉
%T A GPU Unified Platform to Secure Big Data Transportation Using an Error-Prone Elliptic Curve Cryptography
%U https://khub.utp.edu.my/scholars/16017/
%V 1174
%X The time and space optimization, protecting data loss and safeguarding privacy as well as integrity are the priorities in any large file transmission system. However, the current literature is lacking to provide such a system which can take care of each of these aspects very strongly in a unified way. Hence, this research proposes a Graphics Processing Unit (GPU)-based error-prone elliptic curve cryptography with an advanced memory management mechanism. It comprises the elliptic curve public-key cryptography (ECC) to offer higher defiance against the various privacy and integrity attacks. It further includes an advanced dual round of error regulator which can sense and spot any numbers of discrete or continuous faults. This error control technique can regenerate the data if there is any accidental loss during transportation. The usages of advanced parallel execution with a GPU and a unique memory management mechanism make it enable to handle required memory and time during the execution very efficiently. The achieved result shows that the projected unified approach offers higher Throughput on every incident which signifies that it is time-efficient. Additionally, it offers higher SNR, Avalanche Effect and a curtailed proportion of Data Loss. It specifies that the suggested mingled technique is proficient to protect privacy, integrity and mishap of data in big data hauling with the efficient management of available internal memory. © 2021, Springer Nature Singapore Pte Ltd.
%Z cited By 0; Conference of 4th International Conference on Data Management, Analytics and Innovation, ICDMAI 2020 ; Conference Date: 17 January 2020 Through 19 January 2020; Conference Code:243959