Enhancing medical image privacy in IoT with bit-plane level encryption using chaotic map Visa övriga samt affilieringar
2025 (Engelska) Ingår i: Frontiers in Computational Neuroscience, E-ISSN 1662-5188, Vol. 19, artikel-id 1591972Artikel i tidskrift (Refereegranskat) Published
Abstract [en]
Introduction: Preserving privacy is a critical concern in medical imaging, especially in resource limited settings like smart devices connected to the IoT. To address this, a novel encryption method for medical images that operates at the bit plane level, tailored for IoT environments, is developed.
Methods: The approach initializes by processing the original image through the Secure Hash Algorithm (SHA) to derive the initial conditions for the Chen chaotic map. Using the Chen chaotic system, three random number vectors are generated. The first two vectors are employed to shuffle each bit plane of the plaintext image, rearranging rows and columns. The third vector is used to create a random matrix, which further diffuses the permuted bit planes. Finally, the bit planes are combined to produce the ciphertext image. For further security enhancement, this ciphertext is embedded into a carrier image, resulting in a visually secured output.
Results: To evaluate the effectiveness of our algorithm, various tests are conducted, including correlation coefficient analysis (C.C < or negative), histogram analysis, key space [(1090)8] and sensitivity assessments, entropy evaluation [E(S) > 7.98], and occlusion analysis.
Conclusion: Extensive evaluations have proven that the designed scheme exhibits a high degree of resilience to attacks, making it particularly suitable for small IoT devices with limited processing power and memory.
Ort, förlag, år, upplaga, sidor Frontiers Media S.A., 2025. Vol. 19, artikel-id 1591972
Nyckelord [en]
bit-level encryption, chaos, Chen chaotic map, image encryption, IoT, meaningful encryption
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Datavetenskap (datalogi)
Identifikatorer URN: urn:nbn:se:umu:diva-242059 DOI: 10.3389/fncom.2025.1591972 ISI: 001512056700001 PubMedID: 40548326 Scopus ID: 2-s2.0-105008717689 OAI: oai:DiVA.org:umu-242059 DiVA, id: diva2:1982327
2025-07-082025-07-082025-07-08 Bibliografiskt granskad