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

2021 ◽  
Author(s):  
Vishesh Kumar Tanwar ◽  
Balasubramanian Raman ◽  
Amitesh Singh Rajput ◽  
Rama Bhargava
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Visual Information ◽  
Low Cost ◽  
Number System ◽  
Privacy Preserving ◽  
Cloud Services ◽  
Binary Number ◽  
Encryption Scheme ◽  
Encrypted Data

<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>

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

2021 ◽  
Author(s):  
Vishesh Kumar Tanwar ◽  
Balasubramanian Raman ◽  
Amitesh Singh Rajput ◽  
Rama Bhargava
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Visual Information ◽  
Low Cost ◽  
Number System ◽  
Privacy Preserving ◽  
Cloud Services ◽  
Binary Number ◽  
Encryption Scheme ◽  
Encrypted Data

<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>

scholarly journals An Efficient Approach Based on Privacy-Preserving Deep Learning for Satellite Image Classification

2021 ◽  
Vol 13 (11) ◽  
pp. 2221
Author(s):  
Munirah Alkhelaiwi ◽  
Wadii Boulila ◽  
Jawad Ahmad ◽  
Anis Koubaa ◽  
Maha Driss
Keyword(s):  
Deep Learning ◽  
Data Privacy ◽  
Satellite Images ◽  
Homomorphic Encryption ◽  
Satellite Image ◽  
Privacy Preserving ◽  
Cloud Services ◽  
Encryption Scheme ◽  
Privacy Concerns ◽  
Efficient Approach

Satellite images have drawn increasing interest from a wide variety of users, including business and government, ever since their increased usage in important fields ranging from weather, forestry and agriculture to surface changes and biodiversity monitoring. Recent updates in the field have also introduced various deep learning (DL) architectures to satellite imagery as a means of extracting useful information. However, this new approach comes with its own issues, including the fact that many users utilize ready-made cloud services (both public and private) in order to take advantage of built-in DL algorithms and thus avoid the complexity of developing their own DL architectures. However, this presents new challenges to protecting data against unauthorized access, mining and usage of sensitive information extracted from that data. Therefore, new privacy concerns regarding sensitive data in satellite images have arisen. This research proposes an efficient approach that takes advantage of privacy-preserving deep learning (PPDL)-based techniques to address privacy concerns regarding data from satellite images when applying public DL models. In this paper, we proposed a partially homomorphic encryption scheme (a Paillier scheme), which enables processing of confidential information without exposure of the underlying data. Our method achieves robust results when applied to a custom convolutional neural network (CNN) as well as to existing transfer learning methods. The proposed encryption scheme also allows for training CNN models on encrypted data directly, which requires lower computational overhead. Our experiments have been performed on a real-world dataset covering several regions across Saudi Arabia. The results demonstrate that our CNN-based models were able to retain data utility while maintaining data privacy. Security parameters such as correlation coefficient (−0.004), entropy (7.95), energy (0.01), contrast (10.57), number of pixel change rate (4.86), unified average change intensity (33.66), and more are in favor of our proposed encryption scheme. To the best of our knowledge, this research is also one of the first studies that applies PPDL-based techniques to satellite image data in any capacity.

scholarly journals Outsourcing Set Intersection Computation Based on Bloom Filter for Privacy Preservation in Multimedia Processing

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Hongliang Zhu ◽  
Meiqi Chen ◽  
Maohua Sun ◽  
Xin Liao ◽  
Lei Hu
Keyword(s):  
Cloud Computing ◽  
Homomorphic Encryption ◽  
Low Cost ◽  
Bloom Filter ◽  
Privacy Preserving ◽  
Cloud Services ◽  
Multimedia Application ◽  
Computation Method ◽  
Multimedia Processing ◽  
Set Intersection

With the development of cloud computing, the advantages of low cost and high computation ability meet the demands of complicated computation of multimedia processing. Outsourcing computation of cloud could enable users with limited computing resources to store and process distributed multimedia application data without installing multimedia application software in local computer terminals, but the main problem is how to protect the security of user data in untrusted public cloud services. In recent years, the privacy-preserving outsourcing computation is one of the most common methods to solve the security problems of cloud computing. However, the existing computation cannot meet the needs for the large number of nodes and the dynamic topologies. In this paper, we introduce a novel privacy-preserving outsourcing computation method which combines GM homomorphic encryption scheme and Bloom filter together to solve this problem and propose a new privacy-preserving outsourcing set intersection computation protocol. Results show that the new protocol resolves the privacy-preserving outsourcing set intersection computation problem without increasing the complexity and the false positive probability. Besides, the number of participants, the size of input secret sets, and the online time of participants are not limited.

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

2020 ◽  
Author(s):  
Vishesh Kumar Tanwar ◽  
Balasubramanian Raman ◽  
Rama Bhargava
Keyword(s):  
Low Cost ◽  
Privacy Preserving ◽  
Input Image ◽  
Cloud Services ◽  
Pixel Intensity ◽  
Object Removal ◽  
Correlation Attack ◽  
Cloud Server ◽  
Unbiased Manner ◽  
Shamir Secret Sharing Scheme

<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>

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

2020 ◽  
Author(s):  
Vishesh Kumar Tanwar ◽  
Balasubramanian Raman ◽  
Rama Bhargava
Keyword(s):  
Low Cost ◽  
Privacy Preserving ◽  
Input Image ◽  
Cloud Services ◽  
Pixel Intensity ◽  
Object Removal ◽  
Correlation Attack ◽  
Cloud Server ◽  
Unbiased Manner ◽  
Shamir Secret Sharing Scheme

<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>

Deep Learning Approaches for Detecting COVID-19 from Chest X-ray Images: A Survey (Preprint)

2020 ◽  
Author(s):  
Hanan Alghamdi ◽  
Ghada Amoudi ◽  
Salma Elhag ◽  
Kawther Saeedi ◽  
Jomanah Nasser
Keyword(s):  
Deep Learning ◽  
Visual Information ◽  
Low Cost ◽  
Limited Resource ◽  
Learning Approaches ◽  
X Ray ◽  
Wide Range ◽  
Control And Prevention ◽  
Chest X Ray ◽  
Learning Architectures

UNSTRUCTURED Chest X-ray (CXR) imaging is a standard and crucial examination method used for suspected cases of coronavirus disease (COVID-19). In profoundly affected or limited resource areas, CXR imaging is preferable owing to its availability, low cost, and rapid results. However, given the rapidly spreading nature of COVID-19, such tests could limit the efficiency of pandemic control and prevention. In response to this issue, artificial intelligence methods such as deep learning are promising options for automatic diagnosis because they have achieved state-of-the-art performance in the analysis of visual information and a wide range of medical images. This paper reviews and critically assesses the preprint and published reports between March and May 2020 for the diagnosis of COVID-19 via CXR images using convolutional neural networks and other deep learning architectures. Despite the encouraging results, there is an urgent need for public, comprehensive, and diverse datasets. Further investigations in terms of explainable and justifiable decisions are also required for more robust, transparent, and accurate predictions

scholarly journals Implementation of Special Function Unit for Vertex Shader Processor Using Hybrid Number System

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Avni Agarwal ◽  
P. Harsha ◽  
Swati Vasishta ◽  
S. Sivanantham
Keyword(s):  
High Performance ◽  
Special Function ◽  
Low Cost ◽  
Number System ◽  
Binary Number ◽  
Processing Unit ◽  
Function Unit ◽  
Computationally Intensive ◽  
Standard Documents ◽  
Hybrid Number

The world of 3D graphic computing has undergone a revolution in the recent past, making devices more computationally intensive, providing high-end imaging to the user. The OpenGL ES Standard documents the requirements of graphic processing unit. A prime feature of this standard is a special function unit (SFU), which performs all the required mathematical computations on the vertex information corresponding to the image. This paper presents a low-cost, high-performance SFU architecture with improved speed and reduced area. Hybrid number system is employed here in order to reduce the complexity of operations by suitably switching between logarithmic number system (LNS) and binary number system (BNS). In this work, reduction of area and a higher operating frequency are achieved with almost the same power consumption as that of the existing implementations.

scholarly journals A Visual Aquaculture System Using a Cloud-Based Autonomous Drones

Drones ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 109
Author(s):  
Naomi A. Ubina ◽  
Shyi-Chyi Cheng ◽  
Hung-Yuan Chen ◽  
Chin-Chun Chang ◽  
Hsun-Yu Lan
Keyword(s):  
Deep Learning ◽  
Low Cost ◽  
Cost Effective ◽  
Cloud Services ◽  
Fish Length ◽  
Surveillance Systems ◽  
Feeding Intensity ◽  
Fish Feeding ◽  
Intelligent Decision Making ◽  
Intelligent Decision

This paper presents a low-cost and cloud-based autonomous drone system to survey and monitor aquaculture sites. We incorporated artificial intelligence (AI) services using computer vision and combined various deep learning recognition models to achieve scalability and added functionality, in order to perform aquaculture surveillance tasks. The recognition model is embedded in the aquaculture cloud, to analyze images and videos captured by the autonomous drone. The recognition models detect people, cages, and ship vessels at the aquaculture site. The inclusion of AI functions for face recognition, fish counting, fish length estimation and fish feeding intensity provides intelligent decision making. For the fish feeding intensity assessment, the large amount of data in the aquaculture cloud can be an input for analysis using the AI feeding system to optimize farmer production and income. The autonomous drone and aquaculture cloud services are cost-effective and an alternative to expensive surveillance systems and multiple fixed-camera installations. The aquaculture cloud enables the drone to execute its surveillance task more efficiently with an increased navigation time. The mobile drone navigation app is capable of sending surveillance alerts and reports to users. Our multifeatured surveillance system, with the integration of deep-learning models, yielded high-accuracy results.

Sign in / Sign up

Export Citation Format

Share Document