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A Multiple-Medical-Image Encryption Method Based on SHA-256 and DNA Encoding.

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  • 1Department of Applied Mathematics, Xi'an University of Technology, Xi'an 710049, China.

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Summary
This summary is machine-generated.

This study introduces a novel lightweight encryption method for multiple medical images, offering secure and efficient data protection with a single operation. The approach ensures privacy against noise and data loss, crucial for digital medical imaging security.

Keywords:
DNA encodinghash functionlinear congruencemedical image encryptionmultiple-image encryption

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Area of Science:

  • Medical Imaging
  • Cybersecurity
  • Digital Health

Background:

  • The rapid advancement of smart medical technology has increased the volume of digital medical images, raising concerns about privacy and security.
  • Ensuring the confidentiality of sensitive patient data within medical images is a critical challenge in networked healthcare systems.

Purpose of the Study:

  • To develop a lightweight, efficient, and secure encryption method for multiple medical images of varying sizes.
  • To address the computational cost and security vulnerabilities associated with traditional medical image encryption.

Main Methods:

  • A novel padding and stacking technique to create a uniform superimposed image from multiple variable-sized medical images.
  • Generation of a secure encryption key sequence using SHA-256 and a linear congruence algorithm.
  • Encryption of the superimposed image using the generated key sequence and DNA encoding.

Main Results:

  • The proposed method can encrypt/decrypt multiple medical images with a computational cost comparable to single-image encryption.
  • The encryption algorithm demonstrates high security and robustness against noise interference and data loss.
  • A secure, independent decryption mechanism minimizes information leakage risks.

Conclusions:

  • The developed lightweight multiple-image encryption approach provides an effective solution for securing digital medical images.
  • The method offers a practical and secure way to protect patient privacy in the era of digital healthcare and big data.
  • The algorithm's resilience to common image interferences enhances its applicability in real-world medical scenarios.