2022
DOI: 10.1002/int.22890
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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… Show more

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Cited by 8 publications
(3 citation statements)
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References 33 publications
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“…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.…”
Section: Pure Image Encryption-based Imagementioning
confidence: 99%
“…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.…”
Section: Pure Image Encryption-based Imagementioning
confidence: 99%
“…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.…”
Section: Introductionmentioning
confidence: 99%
“…(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.…”
mentioning
confidence: 99%