Privacy-Enhanced Similarity Search Scheme for Cloud Image Databases

Líu, Hao; Goto, Hideaki

doi:10.1587/transinf.2016edl8141

Cited by 3 publications

(1 citation statement)

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“…The scheme converted the image into the feature vectors, and used the similarity matching algorithm in the text encrypted neighborhood to retrieve the target image. Liu and Go [19] proposed a privacy-enhanced scheme for image similarity search in cloud image database. Although the security of encrypted image retrieval was improved in this scheme, the search efficiency was not optimal.…”

Section: Introductionmentioning

confidence: 99%

An Encrypted Image Retrieval Method Based on Harris Corner Optimization and LSH in Cloud Computing

Qin

Xiang

et al. 2019

IEEE Access

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The encrypted image retrieval in cloud computing is a key technology to realize the massive images of storage and management and images safety. In this paper, a novel feature extraction method for encrypted image retrieval is proposed. First, the improved Harris algorithm is used to extract the image features. Next, the Speeded-Up Robust Features algorithm and the Bag of Words model are applied to generate the feature vectors of each image. Then, Local Sensitive Hash algorithm is applied to construct the searchable index for the feature vectors. The chaotic encryption scheme is utilized to protect images and indexes security. Finally, secure similarity search is executed on the cloud server. The experimental results show that compared with the existing encryption retrieval schemes, the proposed retrieval scheme not only reduces the time consumption but also improves the image retrieval accuracy. INDEX TERMS Cloud computing, image retrieval, Harris corner detection, local sensitive hash.

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Section: Introductionmentioning

confidence: 99%