A Comprehensive Study on the Homomorphic Encryption for Secure Image Data Processing

Authors

  • Qiang Chen College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia | Dongguan City University, Dongguan, Guangdong, China
  • Huixian Li College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia | College of Financial Technology, Hebei Finance University, Baoding, Hebei, China
  • Suriyani Binti Ariffin College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia
  • Nor Atiqah Bte Mustapa College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia
Volume: 15 | Issue: 2 | Pages: 21783-21790 | April 2025 | https://doi.org/10.48084/etasr.10007

Abstract

In the contemporary digital landscape, the integrity and confidentiality of data have become paramount concerns. This study presents a comprehensive framework for secure image data processing using homomorphic encryption. The proposed approach involves image preprocessing, logistic regression model training, feature extraction, and polynomial approximation to accommodate the constraints of homomorphic encryption algorithms. Sensitive data, encrypted via homomorphic algorithms, is embedded within images to ensure its concealment during computational operations. Subsequent encryption of the image using the asymmetric Rivest-Shamir-Adleman (RSA) algorithm further secures the encapsulated sensitive data. Through experimental data and analysis, the performance and speed of homomorphic encryption are compared against traditional methods, validating its efficacy in encrypted image data processing.

Keywords:

homomorphic encryption, logistic regression, information security

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References

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How to Cite

[1]
Chen, Q., Li, H., Ariffin, S.B. and Mustapa, N.A.B. 2025. A Comprehensive Study on the Homomorphic Encryption for Secure Image Data Processing. Engineering, Technology & Applied Science Research. 15, 2 (Apr. 2025), 21783–21790. DOI:https://doi.org/10.48084/etasr.10007.

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