A secure medical image encryption technique based on DNA cryptography with elliptic curves
Abstract Health services and telemedicine have proven to be an important area for information protection in research, especially with medical services and smart health care applications. In these systems, medical imaging protection are important not only for clinical diagnosis, but also to protect t...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-06-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-03898-5 |
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| Summary: | Abstract Health services and telemedicine have proven to be an important area for information protection in research, especially with medical services and smart health care applications. In these systems, medical imaging protection are important not only for clinical diagnosis, but also to protect the very sensitive and confidential patient data. With progress in imaging technologies and biomedical processing algorithms, the amount of image data increases rapidly. However, securing this information while transferring through insecure channel is still a constant challenge. Existing encryption techniques often face limitations such as high computational complexity, insufficient security against advanced cryptographic attacks, poor reversal and pixel correlation. To overcome these challenges, the proposed approach provides an innovative hybrid encryption technique that integrates DNA cryptography with Elliptical Curve Cryptography (ECC). The DNA-based coding shows high randomness and equality while the ECC provides strong security and confidentiality. The DNA encoding and secure key generation are employed in the proposed technique to obtain the encrypted medical image. The combination of these techniques addresses the main boundaries of existing disadvantage by increasing both security and calculation efficiency, making it well suited for real time medical applications. The experimental analysis was carried out with various parameters like histogram analysis, correlation coefficient, Chi square, MSE, PSNR, entropy etc. The result analysis states that the proposed methodology outperforms the state-of-the-art existing methods with enhanced performance such as entropy of 7.9981, Correlation coefficient of 0.0019 and PSNR of 53.97. Also, the proposed methodology is tested for runtime analysis, memory analysis and security analysis. |
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| ISSN: | 2045-2322 |