scholarly journals Deniable Authentication Encryption for Privacy Protection using Blockchain

2021 ◽  
Vol 3 (3) ◽  
pp. 259-271
Author(s):  
C. Vijesh Joe ◽  
Jennifer S. Raj
Keyword(s):  
Privacy Protection ◽  
Medical Image ◽  
Image Data ◽  
Security And Privacy ◽  
Authenticated Encryption ◽  
Computing Power ◽  
Plain Image ◽  
Tampered Image ◽  
Cloud Applications ◽  
And Storage

Cloud applications that work on medical data using blockchain is used by managers and doctors in order to get the image data that is shared between various healthcare institutions. To ensure workability and privacy of the image data, it is important to verify the authenticity of the data, retrieve cypher data and encrypt plain image data. An effective methodology to encrypt the data is the use of a public key authenticated encryption methodology which ensures workability and privacy of the data. But, there are a number of such methodologies available that have been formulated previously. However, the drawback with those methodologies is their inadequacy in protecting the privacy of the data. In order to overcome these disadvantages, we propose a searchable encryption algorithm that can be used for sharing blockchain- based medical image data. This methodology provides traceability, unforgettable and non-tampered image data using blockhain technology, overcoming the drawbacks of blockchain such as computing power and storage. The proposed work will also sustain keyword guessing attacks apart from verification of authenticity and privacy protection of the image data. Taking these factors into consideration, it is determine that there is much work involved in providing stronger security and protecting privacy of data senders. The proposed methodology also meets the requirement of indistinguishability of trapdoor and ciphertext. The highlights of the proposed work are its capability in improving the performance of the system in terms of security and privacy protection.

scholarly journals Automatic classification of medical image modality and anatomical location using convolutional neural network

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253205
Author(s):  
Chen-Hua Chiang ◽  
Chi-Lun Weng ◽  
Hung-Wen Chiu
Keyword(s):  
Medical Image ◽  
Brain Mri ◽  
Image Data ◽  
Anatomical Location ◽  
Patient Demographics ◽  
Group Format ◽  
Abdomen Ct ◽  
And Storage ◽  
Image Modality

Modern radiologic images comply with DICOM (digital imaging and communications in medicine) standard, which, upon conversion to other image format, would lose its image detail and information such as patient demographics or type of image modality that DICOM format carries. As there is a growing interest in using large amount of image data for research purpose and acquisition of large amount of medical image is now a standard practice in the clinical setting, efficient handling and storage of large amount of image data is important in both the clinical and research setting. In this study, four classes of images were created, namely, CT (computed tomography) of abdomen, CT of brain, MRI (magnetic resonance imaging) of brain and MRI of spine. After converting these images into JPEG (Joint Photographic Experts Group) format, our proposed CNN architecture could automatically classify these 4 groups of medical images by both their image modality and anatomic location. We achieved excellent overall classification accuracy in both validation and test sets (> 99.5%), specificity and F1 score (> 99%) in each category of this dataset which contained both diseased and normal images. Our study has shown that using CNN for medical image classification is a promising methodology and could work on non-DICOM images, which could potentially save image processing time and storage space.

scholarly journals Editorial for special issue on ”security and privacy protection in the era of IoT devices”

2021 ◽  
Vol 7 (2) ◽  
pp. 245-246
Author(s):  
Weizhi Meng ◽  
Daniel Xiapu Luo ◽  
Chunhua Su ◽  
Debiao He ◽  
Marios Anagnostopoulos ◽  
...  
Keyword(s):  
Privacy Protection ◽  
Security And Privacy ◽  
Special Issue ◽  
Iot Devices

Biometrics Security and Privacy Protection [From the Guest Editors]

2015 ◽  
Vol 32 (5) ◽  
pp. 17-18 ◽  
Author(s):  
Nicholas Evans ◽  
Sebastien Marcel ◽  
Arun Ross ◽  
Andrew Beng Jin Teoh
Keyword(s):  
Privacy Protection ◽  
Security And Privacy

scholarly journals Improvement of Multiparametric MR Image Segmentation by Augmenting the Data With Generative Adversarial Networks for Glioma Patients

2021 ◽  
Vol 14 ◽  
Author(s):  
Eric Nathan Carver ◽  
Zhenzhen Dai ◽  
Evan Liang ◽  
James Snyder ◽  
Ning Wen
Keyword(s):  
Medical Image ◽  
Medical Image Analysis ◽  
Model Performance ◽  
Image Data ◽  
Generative Adversarial Networks ◽  
Dice Similarity Coefficient ◽  
Generative Adversarial Network ◽  
Post Contrast ◽  
Adversarial Network ◽  
Fluid Attenuated Inversion Recovery

Every year thousands of patients are diagnosed with a glioma, a type of malignant brain tumor. MRI plays an essential role in the diagnosis and treatment assessment of these patients. Neural networks show great potential to aid physicians in the medical image analysis. This study investigated the creation of synthetic brain T1-weighted (T1), post-contrast T1-weighted (T1CE), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (Flair) MR images. These synthetic MR (synMR) images were assessed quantitatively with four metrics. The synMR images were also assessed qualitatively by an authoring physician with notions that synMR possessed realism in its portrayal of structural boundaries but struggled to accurately depict tumor heterogeneity. Additionally, this study investigated the synMR images created by generative adversarial network (GAN) to overcome the lack of annotated medical image data in training U-Nets to segment enhancing tumor, whole tumor, and tumor core regions on gliomas. Multiple two-dimensional (2D) U-Nets were trained with original BraTS data and differing subsets of the synMR images. Dice similarity coefficient (DSC) was used as the loss function during training as well a quantitative metric. Additionally, Hausdorff Distance 95% CI (HD) was used to judge the quality of the contours created by these U-Nets. The model performance was improved in both DSC and HD when incorporating synMR in the training set. In summary, this study showed the ability to generate high quality Flair, T2, T1, and T1CE synMR images using GAN. Using synMR images showed encouraging results to improve the U-Net segmentation performance and shows potential to address the scarcity of annotated medical images.

scholarly journals A Framework for Leveraging Image Security in Cloud with Simultaneous Compression and Encryption Using Compressive Sensing

2021 ◽  
Vol 35 (1) ◽  
pp. 85-91
Author(s):  
Naga Raju Hari Manikyam ◽  
Munisamy Shyamala Devi
Keyword(s):  
Compressive Sensing ◽  
Image Data ◽  
Input Image ◽  
Security And Privacy ◽  
Cloud Infrastructure ◽  
Security Framework ◽  
Image Security ◽  
Compression Performance ◽  
Security Algorithm ◽  
Simultaneous Sensing

In the contemporary era, technological innovations like cloud computing and Internet of Things (IoT) pave way for diversified applications producing multimedia content. Especially large volumes of image data, in medical and other domains, are produced. Cloud infrastructure is widely used to reap benefits such as scalability and availability. However, security and privacy of imagery is in jeopardy when outsourced it to cloud directly. Many compression and encryption techniques came into existence to improve performance and security. Nevertheless, in the wake of emergence of quantum computing in future, there is need for more secure means with multiple transformations of data. Compressive sensing (CS) used in existing methods to improve security. However, most of the schemes suffer from the problem of inability to perform compression and encryption simultaneously besides ending up with large key size. In this paper, we proposed a framework known as Cloud Image Security Framework (CISF) leveraging outsourced image security. The framework has an underlying algorithm known as Hybrid Image Security Algorithm (HISA). It is based on compressive sensing, simultaneous sensing and encryption besides random pixel exchange to ensure multiple transformations of input image. The empirical study revealed that the CISF is more effective, secure with acceptable compression performance over the state of the art methods.

scholarly journals Design and Implementation of Compressed Medical Data over an Authenticated Secured

2020 ◽  
Vol 9 (3) ◽  
pp. 1543-1548
Keyword(s):  
Medical Image ◽  
Research Work ◽  
Health Sector ◽  
Image Data ◽  
Medical Data ◽  
Information Network ◽  
Design And Implementation ◽  
Simulation Results ◽  
Medical Image Data ◽  
Secure Transmission

Ideally, secure transmission of medical image data is one of the major challenges in health sector. The National Health Information Network has to protect the data in confidential manner. Storage is also one of the basic concern along with secure transmission. In this paper we propose an algorithm that supports confidentiality, authentication and integrity implementation of the scrambled data before transmitting on the communication medium. Before communication the data is compressed while keeping data encrypted. The research work demonstrate with simulation results. The results shows that the proposed work effectively maintains confidentiality, authentication and integrity. The experimental results evaluated medical image quality like PSNR, MSE, SC, and NAEetc.

scholarly journals Natural Images Allow Universal Adversarial Attacks on Medical Image Classification Using Deep Neural Networks with Transfer Learning

2021 ◽  
Author(s):  
Akinori Minagi ◽  
Hokuto Hirano ◽  
Kazuhiro Takemoto
Keyword(s):  
Neural Networks ◽  
Image Classification ◽  
Transfer Learning ◽  
Medical Image ◽  
Deep Neural Networks ◽  
Disease Diagnosis ◽  
Natural Images ◽  
Fine Tuning ◽  
Security And Privacy ◽  
Medical Image Classification

Abstract Transfer learning from natural images is well used in deep neural networks (DNNs) for medical image classification to achieve computer-aided clinical diagnosis. Although the adversarial vulnerability of DNNs hinders practical applications owing to the high stakes of diagnosis, adversarial attacks are expected to be limited because training data — which are often required for adversarial attacks — are generally unavailable in terms of security and privacy preservation. Nevertheless, we hypothesized that adversarial attacks are also possible using natural images because pre-trained models do not change significantly after fine-tuning. We focused on three representative DNN-based medical image classification tasks (i.e., skin cancer, referable diabetic retinopathy, and pneumonia classifications) and investigated whether medical DNN models with transfer learning are vulnerable to universal adversarial perturbations (UAPs), generated using natural images. UAPs from natural images are useful for both non-targeted and targeted attacks. The performance of UAPs from natural images was significantly higher than that of random controls, although slightly lower than that of UAPs from training images. Vulnerability to UAPs from natural images was observed between different natural image datasets and between different model architectures. The use of transfer learning causes a security hole, which decreases the reliability and safety of computer-based disease diagnosis. Model training from random initialization (without transfer learning) reduced the performance of UAPs from natural images; however, it did not completely avoid vulnerability to UAPs. The vulnerability of UAPs from natural images will become a remarkable security threat.

Sign in / Sign up

Export Citation Format

Share Document