Neural soft computing based secured transmission of intraoral gingivitis image in e-health care

Abstract

<p><em> </em>In this paper, a key based soft computing transmission of intraoral<strong> </strong>gingivitis image has been proposed without the exchange of common key in between the nodes. Gingivitis has been a type of periodontal disease caused due to bacterial colonization inside the mouth, having the early signs of gum bleeding and inflammations in human beings. In E-health care strata, online transmission of such intraoral images with secured encryption technique is needed. Session key based neural soft computing transmission by the dentists has been proposed in this paper with an eye to preserve patients’ confidentiality factor. To resist the data distortion by the eavesdroppers while on the transmission path, secured transmission in a group of tree parity machines was carried out. Topologically same tree parity machines with equal seed values were used by all users of that specified group. A common session key synchronization method was applied in that group. Intraoral image has been encrypted to generate multiple secret shares. Multiple secrets were transmitted to individual nodes in that group. The original gingivitis image can only be reconstructed upon the merging of threshold number of shares. Regression statistics along with ANOVA analysis were carried out on the result set obtained from the proposed technique. The outcomes of such tests were satisfactory for acceptance.<em></em></p>

Newer post-COVID perspective: Teledental encryption by de-multiplexed perceptrons

“…The key issue as stated in the earlier section has been addressed in this proposed paper. Structurally similar perceptrons are taken at the patient’s and the dentist’s Telecare system [ 8 , 9 ]. The input set and the random weight vectors are same at both ends to achieve the synchronization.…”

“…If the symmetric key used for encryption is somehow compromised by the attackers then the entire patients’ data will be leaked to the unauthorized agents. The implementation of artificial neural networks (ANN) [ 6 , 7 ] is a secure option for data transmission inside the Telecare E-Health system [ 8 , 9 ]. A perceptron is a type of artificial neural network which has been considered in this proposed technique.…”

Cryptographic Engineering on COVID-19 Telemedicine: An Intelligent Transmission Through Recurrent Relation Based Session Key

“…Their technique seems to be very powerful on linear configurations. Sarkar A. et al [33] had proposed soft computing on neural networks for the intraoral information sharing in the electronic medical field. Bhowmik A. et al [34] had proposed a symmetric key based secret sharing of information.…”

Generative adversarial network guided mutual learning based synchronization of cluster of neural networks

“…Kinzel and Kanter [26] and Ruttor et al [42] described that a chance of an attack is declining if the network's weight range get increases and the assailant's calculation cost increases, as its effort increases exponentially, as users' effort becomes polynomial. Sarkar and Mandal [44], Sarkar et al [48], Sarkar et al [46], Sarkar and Mandal [45], Sarkar et al [47] proposed schemes which enhanced the security of the protocol by enhancing the synaptic depths of TPM and henceforth counteracting the attacks of the brute power of the attacker. It is found that the amount of security provided by the TPM synchronization also can be improved by introducing a large set of neurons and entries of each neuron in the hidden layers.…”