Outsourcing of Secure k-Nearest Neighbours Interpolation Method

Kalideen, Muhammad Rifthy; Tuğrul, Bülent

doi:10.14569/ijacsa.2018.090446

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…On the other hand, a homomorphic encryption scheme that allows the data to be processed in the encrypted form is well suited for this type of scenarios. Within this scope, a large number of secure schemes [9][10][11][12] have been proposed that employ an homomorphic encryption to support processing on the encrypted data.…”

Section: Introductionmentioning

confidence: 99%

Privacy-Preserving K-Nearest Neighbour Interpolation Method in an Outsourced Environment

Kalideen¹,

Osmanoglu²,

Tuğrul³

2019

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering

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One of the most emerging computer technologies of this decade is cloud computing that allows data owners to outsource their storage and computing requirements. It enables data owners to avoid the costs of building and maintaining a private storage infrastructure. While outsourcing data to cloud promises significant benefits, it possesses substantial security and privacy concerns, especially when data stored in the cloud is sensitive and confidential, like a business plan. Encrypting the data before outsourcing can ensure privacy. However, it will be very difficult to process the cipher text created by the traditional encryption method. Considering this fact, we propose an efficient protocol that allows a query owner to retrieve the interpolation of the top k records from two different databases that are closest to a query point. Note that the databases are stored in two different cloud service providers in encrypted form. We also show that the proposed protocol ensures the privacy and the security of the data and the query point.

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Section: Introductionmentioning

confidence: 99%

Privacy-Preserving K-Nearest Neighbour Interpolation Method in an Outsourced Environment

Kalideen¹,

Osmanoglu²,

Tuğrul³

2019

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering

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“…As shown in Fig. 3, the nearest neighbor interpolation 200 method [26], [27] combined with a convolutional layer is 201 applied to upsample the feature map after extracting deep 202 feature, and its size changes from H × W to 2H × 2W . 203 We utilized this upsampling operation twice to achieve a 204 4x upsampling effect.…”

mentioning

confidence: 99%

SWCGAN: Generative Adversarial Network Combining Swin Transformer and CNN for Remote Sensing Image Super-Resolution

Mei

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Easy and efficient acquisition of high-resolution remote sensing images is of importance in geographic information systems. Previously, deep neural networks composed of convolutional layers have achieved impressive progress in superresolution reconstruction. However, the inherent problems of the convolutional layer, including the difficulty of modeling the longrange dependency, limit the performance of these networks on super-resolution reconstruction. To address the above problems, we propose a generative adversarial network (GAN) by combining the advantages of the swin transformer and convolutional layers, called SWCGAN. It is different from the previous superresolution models, which are composed of pure convolutional blocks. The essential idea behind the proposed method is to generate high-resolution images by a generator network with a hybrid of convolutional and swin transformer layers and then to use a pure swin transformer discriminator network for adversarial training. In the proposed method, (1) we employ a convolutional layer for shallow feature extraction that can be adapted to flexible input sizes; (2) we further propose the residual dense swin transformer block (RDSTB) to extract deep features for upsampling to generate high-resolution images; and (3) we use a simplified swin transformer as the discriminator for adversarial training. To evaluate the performance of the proposed method, we compare the proposed method with other state-of-the-art methods by utilizing the UCMerced benchmark dataset, and we apply the proposed method to real-world remote sensing images. The results demonstrate that the reconstruction performance of the proposed method outperforms other state-of-the-art methods in most metrics.

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Implementing Digital Signature with RSA algorithm to enhance the data protection in cloud

Evangeline,

Jegan,

W.S

2023

2023 First International Conference on Advances in Electrical, Electronics and Computational Intelligence (ICAEECI)

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Outsourcing of Secure k-Nearest Neighbours Interpolation Method

Cited by 4 publications

References 25 publications

Privacy-Preserving K-Nearest Neighbour Interpolation Method in an Outsourced Environment

Privacy-Preserving K-Nearest Neighbour Interpolation Method in an Outsourced Environment

SWCGAN: Generative Adversarial Network Combining Swin Transformer and CNN for Remote Sensing Image Super-Resolution

Implementing Digital Signature with RSA algorithm to enhance the data protection in cloud

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