Privacy Preserving Image Scaling Using 2D Bicubic Interpolation Over the Cloud

Tanwar, Vishesh Kumar; Rajput, Amitesh Singh; Raman, Balasubramanian; Bhargava, Rama

doi:10.1109/smc.2018.00357

Cited by 10 publications

(7 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It indicates that the proposed scheme efficiently breaks the pixels inter-correlation. Further, the corr values in Crypt-OR are compared with Chong et al Fu et al (2012), Teng et al Teng & Wang (2012) and, Tanwar et al Tanwar et al (2018b). This compactness can be observed in first column of E I 2 (i, j) denote the intensities at pixel (i, j) th -position in E I 1 and E I 2 respectively.…”

Section: Numerical Valuesmentioning

confidence: 76%

Crypt-OR:A privacy-preserving system for exemplar-based object-removal over the cloud

Tanwar¹,

Raman²,

Bhargava³

2020

Preprint

Self Cite

View full text Add to dashboard Cite

<div>Object removal is a technique for removing the undesired object(s) and then fill-in the empty region(s) in an image such that the modified image is visually plausible. The existing algorithms are unable to provide promising results when the region to be removed - has varying textured-neighborhood, is small in size and the depth of the image and, is of specific geometric shapes such as triangle</div><div>and rectangle. In this paper, we proposed a new algorithm by incorporating the merits of partial differential equations (PDEs) and exemplar-based schemes to address these challenges. The data term, which measures the continuity of</div><div>isophotes in exemplar-based methods, is modified by incorporating a regularizer term and partial derivatives up to second order of the input image. This regularizer enhances the strength of isophotes striking the boundary and boosts</div><div>the information propagation in an unbiased manner, in terms of pixel intensity values. Additionally, the low-cost, agility, and accessing flexibility benefits of cloud services have attracted user’s attention today. Besides, users are concerned about utilizing them for their data, as they are supported by untrusted third parties. Addressing these privacy concerns for object-removal in an image over the cloud server, we extended and modified our algorithm to make it compatible for (T; N)-threshold Shamir secret sharing scheme (SSS). This privacy-preserving system is an end-to-end system for object-removal in the ED over the cloud server namely Crypt-OR. Crypt-OR is evaluated by removing synthetically imposed objects in real-images. Further, Crypt-OR has proved to be secure under various pixel-based cryptographic attacks such as frequency-known attack and pixel-correlation attack. </div>

show abstract

Section: Numerical Valuesmentioning

confidence: 76%

Crypt-OR:A privacy-preserving system for exemplar-based object-removal over the cloud

Tanwar¹,

Raman²,

Bhargava³

2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The information leakage can be reduced by considering different combinations of varying values of parameters n and k for different blocks, provided in a bounded range. For instance, the combinations of n [11,21] and k [9,17].…”

Section: Qualitative Resultsmentioning

confidence: 99%

SecureDL: A privacy preserving deep learning model for image recognition over cloud

Tanwar¹,

Raman²,

Rajput³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

<div>The key benefits of cloud services, such as low cost, access flexibility, and mobility, have attracted users worldwide to utilize the deep learning algorithms for developing computer vision tasks. Untrusted third parties maintain these cloud servers, and users are always concerned about sharing their confidential data with them. In this paper, we addressed these concerns for by developing SecureDL, a privacy-preserving image recognition model for encrypted data over cloud. Additionally, we proposed a block-based image encryption scheme to protect images’ visual information. The scheme constitutes an order-preserving permutation ordered binary number system and pseudo-random matrices. The encryption scheme is proved to be secure in a probabilistic viewpoint and through various cryptographic attacks. Experiments are performed for several image recognition datasets, and the achieved recognition accuracy for encrypted data is close with non-encrypted data. SecureDL overcomes the storage, and computational overheads occurred in fully-homomorphic and multi-party computations based secure recognition schemes. </div>

show abstract

“…The existing privacy-preserving systems address the tasks of enhancement, and feature extraction [15,20,21] over the encrypted domain (ED). The scheme proposed by Lathey et al [15,20] is efficient for noise removal, edge sharpening, unsharp masking, and anti-aliasing from an image in ED.…”

Section: Related Workmentioning

confidence: 99%

SecureDL: A privacy preserving deep learning model for image recognition over cloud

Tanwar

Raman

Rajput

et al. 2022

Journal of Visual Communication and Image Representation

View full text Add to dashboard Cite

Privacy Preserving Image Scaling Using 2D Bicubic Interpolation Over the Cloud

Cited by 10 publications

References 12 publications

Crypt-OR:A privacy-preserving system for exemplar-based object-removal over the cloud

Crypt-OR:A privacy-preserving system for exemplar-based object-removal over the cloud

SecureDL: A privacy preserving deep learning model for image recognition over cloud

SecureDL: A privacy preserving deep learning model for image recognition over cloud

Contact Info

Product

Resources

About