The healthcare industry has witnessed significant transformations in e-health services by using mobile-edge computing (MEC) and blockchain to facilitate healthcare operations. Many MEC-blockchain-based schemes have been proposed, but some critical technical challenges still remain, such as low Quality of Services (QoS), data privacy, and system security vulnerabilities. In this article, we propose a new decentralized health architecture, called BEdgeHealth that integrates MEC and blockchain for data offloading and data sharing in distributed hospital networks. First, a data offloading scheme is proposed where mobile devices can offload health data to a nearby MEC server for efficient computation with privacy awareness. Moreover, we design a data-sharing scheme, which enables data exchanges among healthcare users by leveraging blockchain and interplanetary file system. Particularly, a smart contract-based authentication mechanism is integrated with MEC to perform decentralized user access verification at the network edge without requiring any central authority. The real-world experiment results and evaluations demonstrate the effectiveness of the proposed BEdgeHealth architecture in terms of improved QoS with data privacy and security guarantees, compared to the existing schemes.

BEdgeHealth: A Decentralized Architecture for Edge-Based IoMT Networks Using Blockchain

In order to enhance the security of exchanged medical images in telemedicine, we propose in this paper a blind and robust approach for medical image protection. This approach consists in embedding patient information and image acquisition data in the image. This imperceptible integration must generate the least possible distortion. The watermarked image must present the same clinical reading as the original image. The proposed approach is applied in the frequency domain. For this purpose, four transforms were used: discrete wavelets transform, non-subsampled contourlet transform, non-subsampled shearlet transform and discreet cosine transform. All these transforms was combined with Schur decomposition and the watermark bits were integrated in the upper triangular matrix. To obtain a satisfactory compromise between robustness and imperceptibility, the integration was performed in the medium frequencies of the image. Imperceptibility and robustness experimental results shows that the proposed methods maintain a high quality of watermarked images and are remarkably robust against several conventional attacks.

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A robust blind medical image watermarking approach for telemedicine applications

Deep learning methods can play a crucial role in anomaly detection, prediction, and supporting decision making for applications like personal health-care, pervasive body sensing, etc. However, current architecture of deep networks suffers the privacy issue that users need to give out their data to the model (typically hosted in a server or a cluster on Cloud) for training or prediction. This problem is getting more severe for those sensitive health-care or medical data (e.g fMRI or body sensors measures like EEG signals). In addition to this, there is also a security risk of leaking these data during the data transmission from user to the model (especially when it's through Internet). Targeting at these issues, in this paper we proposed a new architecture for deep network in which users don't reveal their original data to the model. In our method, feed-forward propagation and data encryption are combined into one process: we migrate the first layer of deep network to users' local devices, and apply the activation functions locally, and then use "dropping activation output" method to make the output non-invertible. The resulting approach is able to make model prediction without accessing users' sensitive raw data. Experiment conducted in this paper showed that our approach achieves the desirable privacy protection requirement, and demonstrated several advantages over the traditional approach with encryption / decryption

Dropping Activation Outputs with Localized First-layer Deep Network for Enhancing User Privacy and Data Security

In this Post-COVID-19 "New Normal" era, the importance of Telemedicine is immense. In telemedicine, the patients' sensitive medical data security is the matter of highest demands. There exists data transmission between the psychiatric patients and psychiatrists through web-based portal. Cryptography is the essential component where medical data is transmitted securely. Insomnia, Depression, Mood swings, Fear of death, etc are the common mental conditions observed in this COVID-19 pandemic. A symmetric key based on randomization of numbers were generated by a reliable and third party Key Distribution Centre. A set of robust binary sequences of 128 bits long were generated, known as secret session keys, and will be transported to both the parties. The web-portal will capture all the patients' symptoms. Then homeopathy E-Prescriptions would be generated by the psychiatrists. This transmission of E-Prescription would be done using the proposed secret session key using Advanced Encryption Standards. Ignatia, Natrum Sulphuricum, Aconite, Arsenicum Album, Belladonna, etc are the important homeopathy medicines as prescribed by the homeopathy psychiatrists in COVID-19. Mathematical calculations were done on this proposed technique with positive efficacy. Hence, homeopathy telemedicine is the pivotal way to cure psychiatric patients remotely in this Post-COVID-19 "New Normal" context.

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Pivotal "New Normal" Telemedicine: secured psychiatric homeopathy medicine transmission in Post-COVID.

In the present global scenario, social distancing is an inevitable one. The need for social distancing and advancements of technology to facilitate the patients and doctors around the world mandated the telemedicine and remote monitoring of patience details as the pivotal way to diagnose the disease. In this, it is essential to transmit the patient’s information such as X-ray and scan images of them to the doctor in the remote location. Preventing the medical data from the technological adversaries is the need of the hour. Infinitesimal attacks in medical images may cost human lives. This work proposes a lightweight, secure medical image encryption scheme for the remote monitoring of health data. The proposed encryption scheme uses computationally less complex weighted shift approximate adder (WSAA)–based encryption logic. The scheme uses a 256-bit key for the encryption process that strengthens the encryption and robust against various attacks. The proposed encryption scheme deploys the WSAA for diffusing the pixel values. A unique way of key distribution for pixel-wise encryption within the image is proposed that avoids the need for separate logic for the pixel-wise confusion. The proposed Encryption scheme is evaluated for its entropy and horizontal, vertical, diagonal correlation, histogram, key space, and sensitivity. Experimental results affirm that the proposed scheme significantly good with less computational complexity. The peak signal-to-noise ratio (PSNR) value of the decrypted image is infinity, and this matches the ideal requirement of the medical encryption scheme.

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A Novel Random Error Approximate Adder-Based Lightweight Medical Image Encryption Scheme for Secure Remote Monitoring of Health Data

In medical field, securing every patient’s record is main concern, ascribed to many fraudulent cases occurring in the health sector. The data of every individual must be engraved and sent into end-user without any issues. Mainly in the healthcare industry, where thoughts are often focused on saving someone’s life and rightly so, but securing access to interfaces and computer systems that store private data like medical records is also an essential factor to consider. Data security is a corresponding action between controlling access to information while allowing free and easy access to those who need that information. Still few problems are focused by the physician in the health sector. Patient’s data should be kept securely in medical provider servers so that physicians can provide proper treatments. To ensure secure storage and access management, we propose a novel hybrid lightweight encryption using swarm optimization algorithm (HLE–SO).The proposed HLE–SO technique merge Paillier encryption and KATAN algorithm, which provides the lightweight features. Generally, the lightweight encryption algorithms are affected by the key space. We introduce the swarm optimization algorithm to optimize the key space by changing the number of iteration round. Our main goal is to encrypt the medical data (EEG signal) and send to end user by utilizing proposed HLE–SO method. Finally, the implementation is done with MATLAB tool with different EEG signal data set. The simulation results of proposed HLE–SO technique is compared with the existing state-of-art techniques in terms of different performance metrics are MSE, PSNR, SSIM, PRD, encryption time and decryption time.

Medical Data Security for Healthcare Applications Using Hybrid Lightweight Encryption and Swarm Optimization Algorithm

The rapid development of Autonomous Vehicles (AVs) increases the requirement for the accurate prediction of objects in the vicinity to guarantee safer journeys. For effectively predicting objects, sensors such as Three-Dimensional Light Detection and Ranging (3D LiDAR) and cameras can be used. The 3D LiDAR sensor captures the 3D shape of the object and produces point cloud data that describes the geometrical structure of the object. The LiDAR-only detectors may be subject to false detection or even non-detection over objects located at high distances. The camera sensor captures RGB images with sufficient attributes that describe the distinct identification of the object. The high-resolution images produced by the camera sensor benefit the precise classification of the objects. However, hindrances such as the absence of depth information from the images, unstructured point clouds, and cross modalities affect assertion and boil down the environmental perception. To this end, this paper proposes an object detection mechanism that fuses the data received from the camera sensor and the 3D LiDAR sensor (OD-C3DL). The 3D LiDAR sensor obtains point clouds of the object such as distance, position, and geometric shape. The OD-C3DL employs Convolutional Neural Networks (CNN) for further processing point clouds obtained from the 3D LiDAR sensor and the camera sensor to recognize the objects effectively. The point cloud of the LiDAR is enhanced and fused with the image space on the Regions of Interest (ROI) for easy recognition of the objects. The evaluation results show that the OD-C3DL can provide an average of 89 real-time objects for a frame and reduces the extraction time by a recall rate of 94%. The average processing time is 65ms, which makes the OD-C3DL model incredibly suitable for the AVs perception. Furthermore, OD-C3DL provides mean accuracy for identifying automobiles and pedestrians at a moderate degree of difficulty is higher than that of the previous models at 79.13% and 88.76%.

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Real-Time 3D Object Detection and Classification in Autonomous Driving Environment Using 3D LiDAR and Camera Sensors

Sarcastic  Image Detection Using Radial Basis Function

OFDM and MIMO Wireless Communication Performance Measurement Using Enhanced Selective mapping based Partial Transmit Sequences

Online trading simply buys and sells assets on the internet-based commercial platforms of a brokerage. Between the mid-1990s and the late 1990s, the use of online trading skyrocketed thanks to the availability of low-cost high-speed computers and Internet connections. Equity, bonds, mutual funds, all can be traded online. The web-based framework for exchange is a built-in world view to spread financial specialists information about the use of a system by shareholders. The share market offers a number of methods which enable brokers to carry out malicious activities and the people involved in trading are not informed. However, malicious brokers, even if they are encrypted, can learn about the interests of investors if they collude with malicious entities. In this project, we present a trading system which ensures that investors are confidential in the presence of unsuspecting brokers by providing further information on the company they are investing in. Whether every broker can acquire some information about the transaction they conduct in the trade. So investors get a lot of profit and are guaranteed with loss of property

K. Tamilarasi

Papers

Medical Data Security for Healthcare Applications Using Hybrid Lightweight Encryption and Swarm Optimization Algorithm

Real-Time 3D Object Detection and Classification in Autonomous Driving Environment Using 3D LiDAR and Camera Sensors

Sarcastic Image Detection Using Radial Basis Function

OFDM and MIMO Wireless Communication Performance Measurement Using Enhanced Selective mapping based Partial Transmit Sequences

A Secure Trading System using High level Virtual Machine (HLVM) Algorithm