Abstract:
Due to various disease diagnosis, the volume of medical data is rising fast. Also, for telemedicine, while medical image transmits over the public network, the distortion of pixels may cause erroneous disease diagnosis. Here, encryption of the image by multiple chaos-based schemes along with DNA cryptography can be a safeguard. As chaotic schemes are very sensitive to the initial conditions, a small difference in the initial conditions yields entirely uncorrelated sequences that assure the strength of encryption. To get high randomness, several DNA encoding and computing rules are deployed. This thesis proposes a multi-stage chaotic encryption technique for the medical image through Logistic map along with Lorenz attractor and DNA cryptography, where both schemes possess the most significant value of control parameters. Thus, their consecutive deployment generates colossal chaotic sequences that ensure the robustness of the proposed technique. At first, the usage of the Logistic map with SHA-256 hash value generates a chaotic sequence that converts the plain medical image into a confusing image. Now, this sequence is used to create a confusion key to encrypt this blur image. Later on, to overcome the limitations of DNA computing rules and to get high randomness, encode this blur image and Lorenz attractor based key according to DNA encoding rules. These rules are determined randomly from eight encoding rules. Then, execute DNA operations between encoded blur image and Lorenz key using the four DNA computing rules and these rules are also determined by chaotic logistic sequence. Thus, the ultimate cipher is generated. Then, to approve the potency of the cipher, a randomness test according to NIST, security and statistical analyses and comparisons are performed.
Description:
This thesis is submitted to the Department of Computer Science and Engineering, Khulna University of Engineering & Technology in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, September 2020.
Cataloged from PDF Version of Thesis.
Includes bibliographical references (pages 42-47).