School of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Beng' bu, China.
College of Computer and Data Science, Fuzhou University, Fuzhou, China.
PLoS One. 2024 Oct 23;19(10):e0309947. doi: 10.1371/journal.pone.0309947. eCollection 2024.
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.
在本文中，我们提出了一种基于安全神经网络推理的动态可授权密文图像检索方案，有效地提高了图像检索的安全性，同时保护了隐私。为了确保原始图像的隐私性，并在加密图像的索引构建阶段实现无需解密操作的特征提取，我们在索引构建阶段使用安全神经网络进行特征提取。此外，我们引入了一种动态可认证的密文检索算法，通过使用户能够快速灵活地检索授权图像，提高了系统的灵活性和安全性。实验结果表明，与类似的文献方案相比，我们的方案在从上传到检索的整个过程中都保证了数据图像的隐私性。此外，我们的方案在保证安全性的同时确保了数据的可用性，使用户能够方便地进行图像检索操作。尽管根据实验结果，整体效率可能不是最优的，但我们的解决方案通过提供高效和安全的图像检索解决方案，满足了云计算环境中的实际应用需求。
