一种智能电网敏感图像可检索的属性基加密方案

Aiming at the threats to security that sensitive images of smart grid are stolen and abused, and at the problem that the existing ciphertext image retrieval solutions are difficult to achieve both high retrieval rate and accuracy, a retrievable attribute-based encryption scheme for sensitive images of smart grid with multi-feature fusion is proposed. The scheme extracts deep image features through the Dense convolutional network, and respectively extracts three traditional image features including bag-of-words features, colorspace features, and histogram of oriented gradients features. The principal component analysis algorithm is used to fuse and reduce the dimensions of four features to enhance the representative information of the feature vector. Based on the ρ stable locality-sensitive hashing function and the improved secure nearest neighbor algorithm, the security index and the trapdoor are constructed for the sensitive image dataset and the query image, respectively. The ciphertext image retrieval result is obtained by calculating the similarity between the security index and the trapdoor. The ciphertext-policy attribute-based encryption mechanism is combined with searchable encryption, and only the users who meet the access strategy can obtain the plaintext image retrieval results. Theoretical analysis and experimental analysis indicate that in the real power dataset, the key generation time, index generation time and retrieval time of the proposed scheme are all in milliseconds, and the retrieval accuracy is 88.6%. Experimental results of the three comparative schemes indicate that the proposed scheme has the best balance between retrieval efficiency and retrieval accuracy. The fusion of deep features can increase the retrieval accuracy of the three comparative schemes by 5.7%-11.4%. The security analysis shows that the proposed solution can resist known ciphertext attacks, known background attacks and collusion attacks.