Improved authentication and computation of medical data transmission in the secure IoT using hyperelliptic curve cryptography

Autor: Karthik Srinivasan, B. Prasanalakshmi, Shermin Shamsudheen, S. Shridevi, Yu-Chen Hu, K. Murugan
Rok vydání: 2021
Předmět:
Zdroj: The Journal of Supercomputing. 78:361-378
ISSN: 1573-0484
0920-8542
DOI: 10.1007/s11227-021-03861-x
Popis: Data transmission is a great challenge in any network environment. However, medical data collected from IoT devices need to be transmitted at high speed to ensure that the transmitted data are secure. This paper focuses on the security, speed and load of transmission. To prove security, combined steganographic methods involving cryptographic algorithms are used. The proposed model begins by updating two entries, medical image data and medical report data. Digital imaging and communications in medicine image data hold the medical report data to be encrypted and transmitted over the network channel. Although the proposed work follows the conventional method of data transmission from encryption until transmission, an effort has been made to split up the given data without transmitting them as such. As a public cryptography mechanism, the algorithm is also capable of transmission during decryption. The method of this article is genuine in proving its secure actions during the transmission of medical data and medical images. The proposed method justifies its performance when tested in hiding medical transcription data of different sizes varying across 30, 45, 64, 128 and 256 bytes in sample images with an average PSNR ranging from 55 to 70 dB, an MAE averaging from 0.2 to 0.7, and an SSIM, SC and correlation coefficient averaging to 1. This research is proven to work well in a simulation environment, and the results prove the genuine nature of the proposed technique.
Databáze: OpenAIRE
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