Fine-Grained Encrypted Image Retrieval in Cloud Environment

Abstract

With the growing emphasis on privacy awareness, there is an increasing demand for privacy-preserving encrypted image retrieval and secure image storage on cloud servers. Nonetheless, existing solutions exhibit certain shortcomings regarding retrieval accuracy, the capacity to search large images from smaller ones, and the implementation of fine-grained access control. Consequently, to rectify these issues, the YOLOv5 technique is employed for object detection within the image, capturing them as localized images. A trained convolutional neural network (CNN) model extracts the feature vectors from the localized images. To safeguard the encrypted image rules from easy accessibility by third parties, the image is encrypted using ElGamal. In contrast, the feature vectors are encrypted using the skNN method to achieve ciphertext retrieval and then upload this to the cloud. In pursuit of fine-grained access control, a role-based multinomial access control technique is implemented to bestow access rights to local graphs, thereby achieving more nuanced permission management and heightened security. The proposed scheme introduces a comprehensive cryptographic image retrieval and secure access solution, encompassing fine-grained access control techniques to bolster security. Ultimately, the experiments are conducted to validate the proposed solution’s feasibility, security, and accuracy. The solution’s performance across various facets is evaluated through these experiments.

Keywords

• encrypted image retrieval
• fine-grained access control
• YOLOv5
• ElGamal
• convolutional neural network (CNN)
• secure k-nearest neighbor (skNN)