Privacy‐preserving image retrieval in a distributed environment

Abstract

Nowadays, several image-based smart services have been widely used in our daily lives, generating many digital images. Since smart devices outsource digital images to the cloud, researchers prefer to select some desired targets from the massive images within the cloud for analysis and improve smart services. Therefore, protective image retrieval on the cloud has attained maximum concentration for privacy-preserving purposes, and the availability assurance of images on the cloud is also a crucial link. Ensuring image security and availability in the cloud environment and precisely preserving retrieval accuracy is comes as a utilitysecurity dilemma while few existing works have explicitly addressed it. Therefore, this paper proposes privacy-preserving image retrieval in the distributed environment based on the combination of image encryption for similarity search and secret image sharing. On the basis of them, we define two-stage encryption. The first-stage encryption algorithm is introduced by modifying Wolfram's reversible cellular automata-based image encryption, which can create a set of processing images to ensure image security and retrieval accuracy. Then, the second-stage encryption algorithm is put forward based on secret image sharing to improve image security and availability. The color histogram could be extracted from the encrypted images for similarity retrieval, and the shadows could be

Privacy-Preserving Image Retrieval Based on Disordered Local Histograms and Vision Transformer in Cloud Computing

“…Ma et al [45] used an improved DenseNet network to extract semantic features from encrypted images. Zhou et al [46] considered encrypted image retrieval under a distributed environment and extracted color histograms of encrypted images for retrieval. Yu et al [47] frst characterize the image by encrypting the DCT coefcient blocks, then extracting the local Markov features of the encrypted image, and fnally constructing the feature vector of local features by the BOW model.…”

Analytical and Integrated Framework for Privacy Presrvation of Images in Cloud Ecosystem

“…The recent work, e.g. (Cai et al, 2022;Kiya et al, 2022;Zhou et al, 2022) offers some potential guidelines for developing a framework for the privacy preservation of any form of data. According to such guidelines, an alternative solution to conventional encryption techniques is needed.…”

A Generic Cryptographic Deep-Learning Inference Platform for Remote Sensing Scenes

“…(2) With the continuous development of quantum computing [20], traditional cryptographic algorithms are no longer absolutely secure. Watermarking, differential privacy, partially homomorphic encryption [16], [17], [21] and other methods are poor insecurity, unable to resist quantum attacks, and cannot provide the ability of deep computing. (3) The encryption computation remains in the basic addition and multiplication operations, which cannot be combined with the standard deep learning libraries such as TensorFlow or Pytorch to complete the generic secure deep learning computation.…”