Transparent hashing in the encrypted domain for privacy preserving image retrieval

Karthik, Kannan; Kashyap, Sachin

doi:10.1007/s11760-013-0471-0

Cited by 9 publications

(5 citation statements)

References 14 publications

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“…Analogously, F‐Score encrypted feature for the Corel‐1K dataset is addressed, which implies that the correctness of the retrieval result is maintained even in the encrypted domain. Categorical retrieval accuracy of Corel‐1K dataset is shown in Table 4, which shows that retrieval efficiency in terms of P, R, and F of PM ( E ) is satisfactory better than the existing work THED, 28 EDCBIR, 49 PPCE, 24 and ISSEDB 23 …”

Section: Resultsmentioning

confidence: 91%

“…This implies that the proposed work with encrypted feature provides satisfactory security, efficient and effective retrieval scheme. Moreover, its retrieval processing time is far better than THED, 28 EDCBIR, 49 PPCE, 24 and ISSEDB 23 for top 20, 30, and 40 image retrieval shown in Table 10. This implies that the proposed method provides efficient image retrieval with secured image features.…”

Section: Resultsmentioning

confidence: 98%

“…The aim of the proposed work is to retrieve relevant images in the encrypted domain with efficiency close to the plain‐text feature retrieval. For this, Precision (P), Recall (R), and F‐score (F) values are calculated with encrypted feature PM ( E ) and are compared with existing secure CBIR methods THED , 28 EDCBIR , 49 PPCE , 24 and ISSEDB 23 . P and R values obtained for encrypted feature PM ( E ) demonstrate secure effective retrieval, as the P and R values obtained for plaintext feature PM ( P ) and encrypted feature PM ( E ) are close.…”

Section: Resultsmentioning

confidence: 99%

“…Also, for privacy‐preserving in CBIR, the block‐based approaches are effectively tailored in the state‐of‐the‐art. In this context, the authors of Reference 28 demonstrated a privacy‐preserving scheme using DCT‐AC coefficient distribution.…”

Section: Introductionmentioning

confidence: 99%

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Secure content‐based image retrieval using modified Euclidean distance for encrypted features

Majhi

Pal

Islam

et al. 2020

Trans Emerging Tel Tech

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In this article, a secure image retrieval scheme is proposed, which focuses on providing satisfactory retrieval results, and the framework searches relevant images even in an encrypted domain without compromising the performance of the retrieval process. Initially, bit‐level features have been endeavored from the luminance component of the image, from which statistical parameters are computed to generate more intrinsic values. These values are subsequently divided into bins to configure two histograms, which effectively reduce the length of the feature vector. These histograms are then eventually combined with quantized chrominance features to enhance the discriminative property of the feature vector. Since the proposed scheme is in the encrypted domain, conventional similarity measure distance for the image is not well suited. So, a modified Euclidean distance is incorporated, which is modeled to work with encrypted features. To comprehend the security, a piecewise logistic map sequence is considered, where seed values are assimilated to generate two secret keys. As a result, not only the system provides an efficient, secure retrieval system but also cryptographic components have no impact on its retrieval efficiency, and satisfactory results are obtained. Experimental results on Corel‐1K and GHIM‐10K illustrate decent performance in retrieval as compared to existing work in the retrieval domain.

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Section: Resultsmentioning

confidence: 91%

Section: Resultsmentioning

confidence: 98%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Secure content‐based image retrieval using modified Euclidean distance for encrypted features

Majhi

Pal

Islam

et al. 2020

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…Lu et al [14] proposed three distance-preserving methods: bit plane randomization, random projection, and random unary encoding, which are applied on low-level features such as color histogram. Karthik et al [15] proposed a transparent privacypreserving hash method, which keeps the statistical rules of encrypted AC coefficients but ignores the spatial information distribution of the image. Xu et al [16] proposed a secure retrieval method for JPEG images, which preserves the distribution of the AC coefficients and the statistical rule of color after the decoding of encrypted images.…”

Section: Introductionmentioning

confidence: 99%

A Privacy-preserving Image Retrieval Method Based on Improved BoVW Model in Cloud Environment

Gong

Zhao

2018

IETE Technical Review

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With the rapid development of cloud computing technology, more and more users choose to outsource image data to clouds. To protect users' privacy and guarantee data's confidentiality, images need to be encrypted before being outsourced to CSP, but this brings new difficulties to some basic yet important data services, such as content-based image retrieval (CBIR). In this paper, a privacy-preserving image retrieval method based on an improved BoVW model is proposed. An improved BoVW method based on Hamming embedding can provide binary signatures that refine the matching based on visual words; therefore, retrieval precision is improved significantly; orthogonal transformation is utilized to implement privacy-preserving image retrieval, where image features are divided into two different fields with orthogonal decomposition, for which encryption and distance comparison are executed separately, and two kinds of operation results are fused in the final vector with orthogonal composition. As a result, cloud server can extract components from encrypted features directly and compare distance with those of query image, without violating the privacy of images and features. Any algorithm can be used to encrypt features, which enhance the practicability of the proposed method. The security analysis and experimental results prove its security and retrieval performance.

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A Secure JPEG Image Retrieval Method in Cloud Environment

Han

Gong

2016

Cloud Computing and Security

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Transparent hashing in the encrypted domain for privacy preserving image retrieval

Cited by 9 publications

References 14 publications

Secure content‐based image retrieval using modified Euclidean distance for encrypted features

Secure content‐based image retrieval using modified Euclidean distance for encrypted features

A Privacy-preserving Image Retrieval Method Based on Improved BoVW Model in Cloud Environment

A Secure JPEG Image Retrieval Method in Cloud Environment

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