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A dynamic authorizable ciphertext image retrieval algorithm based on security neural network inference.

Xin-Yu Zhang1, Jing-Wei Hong2,3

  • 1School of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Beng' bu, China.

Plos One
|October 23, 2024
PubMed
Summary
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This study introduces a secure image retrieval system using encrypted data and neural networks. It enhances privacy and allows flexible, authorized access to images without decryption.

Area of Science:

  • Computer Science
  • Information Security
  • Artificial Intelligence

Background:

  • Traditional image retrieval systems often compromise data privacy.
  • Secure image retrieval is crucial for cloud computing environments.
  • Existing methods may lack flexibility and dynamic authorization.

Purpose of the Study:

  • To propose a dynamic, authorizable ciphertext image retrieval scheme.
  • To enhance image retrieval security while preserving data privacy.
  • To enable efficient and flexible retrieval of authorized encrypted images.

Main Methods:

  • Utilizing secure neural network inference for feature extraction on encrypted images.
  • Implementing a dynamic, authenticatable ciphertext retrieval algorithm.

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  • Developing an index construction stage for encrypted image features.

    • Main Results:

    • Guaranteed data image privacy throughout the upload-to-retrieval process.
    • Ensured data availability and security for convenient image retrieval.
    • Demonstrated a practical solution for cloud computing environments, despite potential efficiency limitations.

    Conclusions:

    • The proposed scheme effectively enhances image retrieval security and privacy.
    • Dynamic authorization and secure inference provide flexibility and robust protection.
    • The solution meets practical needs for secure cloud-based image retrieval.