Securing Medical Data with Blockchain Encryption

By Dr Silpaja Chandrasekar, PhDMar 18 2024Reviewed by Susha Cheriyedath, M.Sc.

In a paper published in the journal Scientific Reports, researchers introduced a novel blockchain-based chaotic Arnold's cat map encryption scheme (BCAES), which combined Blockchain and Arnold's cat map encryption. This scheme aimed to enhance the security of medical data transmission and storage in the cloud.

The proposed method ensured data integrity and authenticity by encrypting images using Arnold's cat map and storing signed documents in the blockchain. Various analyses demonstrated the efficiency of BCAES, surpassing previous encryption methods in terms of security and performance.

Related Work

In previous efforts to secure internet transactions and data storage, the emergence of cloud computing has played a significant role, particularly in industries like telemedicine. With the rising exchange of medical data online, especially images, ensuring their integrity and security is vital.

Traditional encryption methods prove inadequate for rapid image encryption due to computational demands, leading to the exploration of chaotic theory-based encryption. However, concerns persist regarding data authenticity and integrity, prompting the integration of blockchain technology. Blockchain's decentralized and tamper-resistant nature offers a promising solution, particularly when combined with cloud storage, enabling robust management of electronic health records without reliance on centralized authorities.

Blockchain-based Medical Image Encryption

The proposed BCAES comprises five components: the data sender, cloud server (CS), data user, blockchain, and encryption/decryption process. Initially, the sender encrypts medical images and generates a digital signature using the secure hash algorithm 256-bit (SHA-256) hash function. The cloud server stores the encrypted image, while the blockchain saves the signed document. When a data user requests the medical image, the cloud server provides the encrypted file, which the data user decrypts using Arnold's cat map decryption process. The decrypted file is sent to the blockchain for validation to ensure image integrity and authenticity, which responds with a verification message.

The data sender encrypts medical images using Arnold's cat map encryption scheme and sends them to the cloud server. Additionally, the sender signs the encrypted image, ensuring its authenticity and integrity, and stores it in the blockchain network. The cloud server is a storage repository for massive medical image data and responds to data user requests by providing relevant encrypted files.

Data users, typically healthcare professionals, request encrypted images from the cloud server and validate their authenticity by cross-referencing the ciphertext's ID stored in the blockchain. The blockchain uses the util ciphertext's-1 algorithm to create a digital signature, which is validated when a data user requests verification. If the signature matches, the blockchain confirms the authenticity of the ciphertext.

The encryption/decryption process involves encrypting medical images using Arnold's cat map with an orthogonal fundamental matrix and Henon map before transmission to the cloud server. The decryption process reverses this encryption method, utilizing Arnold's cat map decryption, Hill cipher, and Henon map to reconstruct the original image from the encrypted data.

Furthermore, the proposed encryption algorithm comprises three phases: confusion, permutation, and diffusion. These phases involve utilizing Henon and Arnold's cat map to permute pixel locations, create an orthogArnold's matrix, and perform pixel diffusion. The decryption process employs reverse encryption to decrypt the image, ensuring that it restores its original form. Finally, signature creation and verification processes provide data authenticity and integrity, with the blockchain as a trusted validation source.

BCAES Comparative Evaluation Summary

Researchers conducted the comparative results and performance evaluation of the proposed BCAES on a desktop computer running Ubuntu 16.04 with an Intel Core i7-6700 processor, utilizing MATLAB 2018a for simulation. The system utilized a private blockchain created using the Geth Ethereum client, leveraging the Ethereum platform known for its performance and reliability.

Sample images from various datasets representing different anatomical regions were employed, each with pixel values of length n. Researchers executed encryption and decryption processes using Arnold's cat map, Henon map, and Hill cipher, resulting in encrypted images of the same size. They conducted security analysis, including critical space analysis, fundamental sensitivity analysis, histogram analysis, chi-square test analysis, information entropy analysis, and correlation analysis of nearby pixels.

Critical space analysis ensured that the encryption scheme's key size was sufficient to thwart brute force attacks, with the proposed scheme relying on three keys. Critical sensitivity analysis demonstrated the encryption method's vulnerability to even minor modifications in secret keys. Information entropy analysis indicated high unpredictability in the generated private keys. Chi-square test analysis showed strong consistency in encrypted image histograms, while histogram analysis confirmed the uniformity of pixel distribution in encrypted images.

Sensitivity analysis revealed that minor changes in the original image could significantly affect the generated private keys, enhancing the system's randomness and uncertainty. Correlation analysis demonstrated low correlation coefficients between neighboring pixels in encrypted images, ensuring attackers' difficulty extracting helpful information. Finally, researchers performed similarity analysis using mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM), showing the effectiveness of the proposed scheme compared to existing techniques.

Time complexity analysis revealed efficient encryption and decryption times for various image resolutions, with the proposed scheme outperforming competing approaches. Differential attack analysis highlighted the scheme's resilience against such attacks, while integrity and confidentiality analysis emphasized protecting healthcare data stored in the cloud through encryption and blockchain technology. Access to encrypted data on the cloud server is restricted to authorized users with the requisite private keys, ensuring data privacy and integrity. Additionally, blockchain signatures ensure data integrity, allowing only authenticated users to decrypt encrypted messages.

Conclusion

To sum up, the proposed BCAES presented a robust solution leveraging blockchain technology to store medical images in the cloud securely. Employing a novel chaotic map-based encryption method ensured data confidentiality, integrity, and authentication. Through comprehensive analysis, including crucial space, sensitivity, and security assessments, the BCAES demonstrated superior protection compared to existing methods. While classical chaotic systems had limitations, future research could explore enhancements through quantification processes.