Crypto-Compression of Medical Images on Neural Cryptography With Queries In Telemedicine System

There is a requisite to secure the medical images from the hacker admittance when the switch over of medical information is taken place among the patients and doctors. We can generate a private key using neural cryptography, which is based on synchronization of Tree Parity Machines (TPMs) by online learning. The random inputs are generated by Pseudo-Random Number Generators (PRNGs). In the proposed TPMs random inputs are replaced with queries are considered. The queries depend on the current state of A and B TPMS. Then, TPMs hidden layer of each output vectors are compared. That is, the output vectors of hidden unit using Hebbian learning rule, left-dynamic hidden unit using Random walk learning rule and right-dynamic hidden unit using Anti-Hebbian learning rule are compared. Among the compared values, one of the best values is received by the output layer. Similarly, the other hidden units, left-dynamic hidden units and right-dynamic hidden units perform the same operations and values are received by the output layer. A private key (256-bit) with padding bits is used for encryption and decryption in Rijndael