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

In this paper, we propose a dynamic authorizable ciphertext image retrieval scheme based on secure neural network inference that effectively enhances the security of image retrieval while preserving privacy. To ensure the privacy of the original image and enable feature extraction without decryption operations, we employ a secure neural network for feature extraction during the index construction stage of encrypted images. Additionally, we introduce a dynamic authenticatable ciphertext retrieval algorithm to enhance system flexibility and security by enabling users to quickly and flexibly retrieve authorized images. Experimental results demonstrate that our scheme guarantees data image privacy throughout the entire process from upload to retrieval compared to similar literature schemes. Furthermore, our scheme ensures data availability while maintaining security, allowing users to conveniently perform image retrieval operations. Although overall efficiency may not be optimal according to experimental results, our solution satisfies practical application needs in cloud computing environments by providing an efficient and secure image retrieval solution.

Informations de copyright

Copyright: © 2024 Zhang, Hong. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.