A CQ Algorithmic Approach to Stealthy Steganography for Concealing Patient's Medical Data

In today's healthcare sector, protecting patient's data from unauthorized access or exposure of sensitive patient information stored on digital servers poses severe risks to patient privacy. This has a significant impact on preserving confidentiality and maintaining public trust in healthcare. A novel steganography technique is proposed utilizing the Censor Elfving (CQ) algorithm to embed confidential patient data within medical images. This offers a strong description for enhancing data security and patient trust. The steganography techniques Least Significant Bit (LSB) and Pixel Value Differencing (PVD) are used in embedding and extracting the medical data securely in the allocated location. A randomly generated key is also being used for further security. Comprehensive assessments of MRI images demonstrate the superior capabilities of the proposed method. Evaluation metrics including Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Structural Similarity Index (SSIM) indicate considerable improvements. Proposed model provides three tiers of security for patient's medical data enhancing PSNR values of up to 66dB for LSB, 61dB for PVD (long string) and 71dB for PVD (short string) indicates a high level of image quality with minimal perceptual differences compared to the original image. This shows remarkable efficiency in hiding medical data within MRI as evidenced by its superior performance across various metrics and image dimensions.