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<p>The increasing use of electronic health records (EHRs) has led to a growing concern about the security of sensitive medical information. Medical images hold critical diagnostic and therapeutic information about patients, making their confidentiality essential, especially in telehealth contexts. Data encryption is essential for protecting sensitive information, especially in the realm of image encryption, which is vital for safeguarding visual data. This paper proposes a new encryption algorithm of EHR medical images using chaotic Henon map systems with self-adaptive approach. This approach creates as many correlated security keys as there are elements contained in the medical images delivered by EHR by means of Plaintext-Associated Key Generation (PAKG). This self-adaptive framework guarantees that the generated key revolves entirely around the characteristics of the plaintext image. For the pixel confusion phase, a block-based generalization technique along with multi-level map is used and for the diffusion phase, the threshold-based generalization technique involving the chaotic Henon map systems is used. Through this method, medical image encryption is made possible with low computational power required thus striking more of the balance between the two factors, efficiency and security. Our presented scheme has been implemented and simulated over standard EHR images; the security aspects have been weighed and measured in terms of entropy, key sensitivity, key space, correlation, and histogram analysis. Finally, comparing the results of our scheme with other existing methods it proven that our scheme performs substantially better in case of similar conditions. As for the strengths of our approach, it is necessary to mention that security keys are generated dynamically for each of the input medical images and the information of the image itself is considered for-key creation. The entropy values closely approximate optimal threshold value 8, affirming the efficiency of the encryption scheme. NPCR values exceed 99%, indicating high sensitivity to pixel changes, while UACI values range from 33.66% to 36.71%, ensuring significant intensity variations in encrypted images. MSE values range from 45.4691 to 55.6730, and PSNR values vary between 17.78 and 29.36&#xa0;dB, signifying effective encryption with an acceptable level of image distortion. This approach provides optimal computational complexity and secure transmission. Thus it is a potential solution for medical image encryption in telemedicine services that ensures both high protection and reasonable effectiveness.</p>

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Securing Telehealth Electronic Health Records with Adaptive Chaotic Encryption and Dynamic Key Generation

  • 期刊论文

  • 发表日期：

    2025年5月2日

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  • Venkatala Venkatappa Leelavathi,
  • Bajanemane Krishnamurthy Rashmi Priyadarshini,
  • Kiran Puttegowda

摘要

The increasing use of electronic health records (EHRs) has led to a growing concern about the security of sensitive medical information. Medical images hold critical diagnostic and therapeutic information about patients, making their confidentiality essential, especially in telehealth contexts. Data encryption is essential for protecting sensitive information, especially in the realm of image encryption, which is vital for safeguarding visual data. This paper proposes a new encryption algorithm of EHR medical images using chaotic Henon map systems with self-adaptive approach. This approach creates as many correlated security keys as there are elements contained in the medical images delivered by EHR by means of Plaintext-Associated Key Generation (PAKG). This self-adaptive framework guarantees that the generated key revolves entirely around the characteristics of the plaintext image. For the pixel confusion phase, a block-based generalization technique along with multi-level map is used and for the diffusion phase, the threshold-based generalization technique involving the chaotic Henon map systems is used. Through this method, medical image encryption is made possible with low computational power required thus striking more of the balance between the two factors, efficiency and security. Our presented scheme has been implemented and simulated over standard EHR images; the security aspects have been weighed and measured in terms of entropy, key sensitivity, key space, correlation, and histogram analysis. Finally, comparing the results of our scheme with other existing methods it proven that our scheme performs substantially better in case of similar conditions. As for the strengths of our approach, it is necessary to mention that security keys are generated dynamically for each of the input medical images and the information of the image itself is considered for-key creation. The entropy values closely approximate optimal threshold value 8, affirming the efficiency of the encryption scheme. NPCR values exceed 99%, indicating high sensitivity to pixel changes, while UACI values range from 33.66% to 36.71%, ensuring significant intensity variations in encrypted images. MSE values range from 45.4691 to 55.6730, and PSNR values vary between 17.78 and 29.36 dB, signifying effective encryption with an acceptable level of image distortion. This approach provides optimal computational complexity and secure transmission. Thus it is a potential solution for medical image encryption in telemedicine services that ensures both high protection and reasonable effectiveness.