Sorting retrieval method for image ciphertexts based on ghost imaging

Abstract In this paper, a sorting retrieval method for image ciphertexts based on ghost imaging is proposed. With this scheme, the ciphertexts can be sorted according to similarity with the base image without decrypting the ciphertexts, and the sorting results obtained are highly consistent with the result of the plaintexts under the same sorting method, it is potential for applications in the field of homomorphic encryption. Differently from the traditional method of retrieval through deep learning, neural network and other models with a lot of pre-training, the proposed method in this paper does not require such training, but directly calculates the similarity between the ciphertexts and sort them, avoiding the additional computational overhead, and eliminating the risk of information leakage during pre-training. The proposed scheme is verified by numerical simulation using three datasets, MNIST, MNIST-L, and MNIST-C, the ciphertext sorting retrieval average accuracies can reach up to 96.5%, 98%, and 98%, respectively, showing the efficiency of this sorted retrieval method for image ciphertexts encrypted based on ghost imaging.