Secure Medical Data Transmission Model for IoT-Based Healthcare Systems

Abstract

Due to the significant advancement of the Internet of Things (IoT) in the healthcare sector, the security, and the integrity of the medical data became big challenges for healthcare services applications. This paper proposes a hybrid security model for securing the diagnostic text data in medical images. The proposed model is developed through integrating either 2-D discrete wavelet transform 1 level (2D-DWT-1L) or 2-D discrete wavelet transform 2 level (2D-DWT-2L) steganography technique with a proposed hybrid encryption scheme. The proposed hybrid encryption schema is built using a combination of Advanced Encryption Standard, and Rivest, Shamir, and Adleman algorithms. The proposed model starts by encrypting the secret data; then it hides the result in a cover image using 2D-DWT-1L or 2D-DWT-2L. Both color and gray-scale images are used as cover images to conceal different text sizes. The performance of the proposed system was evaluated based on six statistical parameters; the peak signal-to-noise ratio (PSNR), mean square error (MSE), bit error rate (BER), structural similarity (SSIM), structural content (SC), and correlation. The PSNR values were relatively varied from 50.59 to 57.44 in case of color images and from 50.52 to 56.09 with the gray scale images. The MSE values varied from 0.12 to 0.57 for the color images and from 0.14 to 0.57 for the gray scale images. The BER values were zero for both images, while SSIM, SC, and correlation values were ones for both images. Compared with the state-of-the-art methods, the proposed model proved its ability to hide the confidential patient's data into a transmitted cover image with high imperceptibility, capacity, and minimal deterioration in the received stego-image.

Challenges and opportunities in IoT healthcare systems: a systematic review

“…Another issue in healthcare monitoring is the security that the data can be easily corrupted by the attacker or hackers. Hence, it is necessary to develop a privacy-preserving IoT based healthcare system and has to be integrated with patients for efficient data transfer [8]. Few pieces of research were involved in improving the security of data transmission in the IoT system [9,10].…”

Optimal feature level fusion based ANFIS classifier for brain MRI image classification

“…[26][27][28][29][30][31][32] ORCID Mohamed Elhoseny http://orcid.org/0000-0001-6347-8368 For identifying and visualizing the tumor in the MRI brain images, two important models such as Feature Selection (FS) and Machine Learning classification techniques were utilized.…”

“…In the future work, we will apply the proposed model on different smart applications to improve its performance. [26][27][28][29][30][31][32] ORCID Mohamed Elhoseny http://orcid.org/0000-0001-6347-8368…”

A medical monitoring scheme and health‐medical service composition model in cloud‐based IoT platform

“…The PPS has to be performed for the operations of (1) filtering the patient's private data before any transferring and uploading it to the outside and (2) allowing the patient to keep control and protecting confidentiality through privacy and security necessities with respect to her sensitive data. For gaining the second objective, the PPS has to satisfy the data protection through a security structure that provides a simple data protection utility via data encryption method for achieving access control . Before any act of uploading to the clouds, all data is encrypted via a symmetric data protection key by PPS to protect it from unauthorized access.…”