Certificateless Public Auditing Scheme With Data Privacy and Dynamics in Group User Model of Cloud-Assisted Medical WSNs.

With the application of wireless sensor network (WSN) in healthcare field, online sharing of medical data has attracted more and more attention. However, wearable sensor nodes are limited in energy, storage space and data processing capacity, which largely restricts their deployment in resource demand application scenarios. Fortunately, cloud storage services can enrich the capabilities of wearable sensors and provide an effective method for people to share data within a group. However, as medical data directly relates to patients' health and privacy information, ensuring the integrity and privacy of medical records stored in cloud servers becomes a key issue to be urgently solved. Many public data auditing schemes have been put forward to address the above issues. Unfortunately, most of them have security vulnerabilities or poor functionality and performance. In this paper, we come up with a secure and efficient certificateless public auditing scheme for cloud-assisted medical WSNs, which not only supports dynamic data sharingand privacy protection, but also achieves efficient group user revocation. Security analysis and performance evaluation demonstrate that our scheme significantly reduce the total computation cost while achieving a higher security level. Compared with other related schemes, our new proposal is more suitable for group user data sharing in cloud-assisted medical WSNs.

Privacy-Preserving Federated Learning for Internet of Medical Things Under Edge Computing.

Edge intelligent computing is widely used in the fields, such as the Internet of Medical Things (IoMT), which has advantages, including high data processing efficiency, strong real-time performance and low network delay. However, there are many problems including privacy disclosure, limited calculation force, as well as scheduling and coordination issues. Federated learning can greatly improves training efficiency. However, due to the sensitive nature of the healthcare data, the aforementioned approach of transferring the patient's data to the servers may create serious security and privacy issues. Therefore, this article proposes a Privacy Protection Scheme for Federated Learning under Edge Computing (PPFLEC). First of all, we propose a lightweight privacy protection protocol based on a shared secret and weight mask, which is based on a random mask scheme of secret sharing. It is more accurate and efficient than,homomorphic encryption. It can not only protect gradient privacy without losing model accuracy, but also resist equipment dropping and collusion attacks between devices. Second, we design an algorithm based on a digital signature and hash function, which achieves the integrity and consistency of the message, as well as resisting replay attacks. Finally, we propose a periodic average training strategy, compared with differential privacy to prove that our scheme is 40 % faster in efficiency than in deferential privacy. Meanwhile, compared with federated learning, we can achieve the same efficiency under the condition of ensuring safety. Therefore, our scheme can work well in unstable edge computing environments such as smart healthcare.

Profile Matching for IoMT: A Verifiable Private Set Intersection Scheme.

The rapid development of the Internet of Things (IoTs), 5 G and artificial intelligence (AI) technology have been dramatically incentivizing the advancement of Internet of Medical Things (IoMT) in recent years. Profile matching technology can be used to realize the sharing of medical information between patients by matching similar symptom attributes. However, the symptom attributes are associated with patients' sensitive information such as gender, age, physiological data, and other personal health information, thus the privacy of patients will be revealed during the matching process in the IoMT. To solve the problem, this paper proposes a verifiable private set intersection scheme to achieve fine-grained profile matching. On the one hand, the privacy data of patients can be divided by multi-tag to implement fine-grained operations. On the other hand, re-encryption technique is utilized to protect the privacy of patients. In addition, the cloud server may violate the scheme, thus a verifiable mechanism is leveraged to check the correctness of computation. The analysis of security indicates that our proposed scheme can resist the untrusted cloud server and the performance simulation demonstrates that our scheme improves efficiency by reducing the use of bilinear pairs.

COVID_SCREENET: COVID-19 Screening in Chest Radiography Images Using Deep Transfer Stacking.

Infectious diseases are highly contagious due to rapid transmission and very challenging to diagnose in the early stage. Artificial Intelligence and Machine Learning now become a strategic weapon in assisting infectious disease prevention, rapid-response in diagnosis, surveillance, and management. In this paper, a bifold COVID_SCREENET architecture is introduced for providing COVID-19 screening solutions using Chest Radiography (CR) images. Transfer learning using nine pre-trained ImageNet models to extract the features of Normal, Pneumonia, and COVID-19 images is adapted in the first fold and classified using baseline Convolutional Neural Network (CNN). A Modified Stacked Ensemble Learning (MSEL) is proposed in the second fold by stacking the top five pre-trained models, and then the predictions resulted. Experimentation is carried out in two folds: In first fold, open-source samples are considered and in second fold 2216 real-time samples collected from Tamilnadu Government Hospitals, India, and the screening results for COVID data is 100% accurate in both the cases. The proposed approach is also validated and blind reviewed with the help of two radiologists at Thanjavur Medical College & Hospitals by collecting 2216 chest X-ray images between the month of April and May. Based on the reports, the measures are calculated for COVID_SCREENET and it showed 100% accuracy in performing multi-class classification.

The Role of Internet of Things to Control the Outbreak of COVID-19 Pandemic.

Currently, COVID-19 pandemic is the major cause of disease burden globally. So, there is a need for an urgent solution to fight against this pandemic. Internet of Things (IoT) has the ability of data transmission without human interaction. This technology enables devices to connect in the hospitals and other planned locations to combat this situation. This article provides a road map by highlighting the IoT applications that can help to control it. This study also proposes a real-time identification and monitoring of COVID-19 patients. The proposed framework consists of four components using the cloud architecture: 1) data collection of disease symptoms (using IoT-based devices); 2) health center or quarantine center (data collected using IoT devices); 3) data warehouse (analysis using machine learning models); and 4) health professionals (provide treatment). To predict the severity level of COVID-19 patients on the basis of IoT-based real-time data, we experimented with five machine learning models. The results reveal that random forest outperformed among all other models. IoT applications will help management, health professionals, and patients to investigate the symptoms of contagious disease and manage COVID-19 +ve patients worldwide.

COVID-19: Automatic Detection of the Novel Coronavirus Disease From CT Images Using an Optimized Convolutional Neural Network.

It is widely known that a quick disclosure of the COVID-19 can help to reduce its spread dramatically. Transcriptase polymerase chain reaction could be a more useful, rapid, and trustworthy technique for the evaluation and classification of the COVID-19 disease. Currently, a computerized method for classifying computed tomography (CT) images of chests can be crucial for speeding up the detection while the COVID-19 epidemic is rapidly spreading. In this article, the authors have proposed an optimized convolutional neural network model (ADECO-CNN) to divide infected and not infected patients. Furthermore, the ADECO-CNN approach is compared with pretrained convolutional neural network (CNN)-based VGG19, GoogleNet, and ResNet models. Extensive analysis proved that the ADECO-CNN-optimized CNN model can classify CT images with 99.99% accuracy, 99.96% sensitivity, 99.92% precision, and 99.97% specificity.

Efficient NTRU Lattice-Based Certificateless Signature Scheme for Medical Cyber-Physical Systems.

An electronic health (e-health) system, such as a medical cyber-physical system, offers a number of benefits (e.g. inform medical diagnosis). There are, however, a number of considerations in the implementation of the medical cyber-physical system, such as the integrity of medical / healthcare data (e.g. manipulated data can result in misdiagnosis). A number of digital signature schemes have been proposed in recent years to mitigate some of these challenges. However, the security of existing signatures is mostly based on conventional difficult mathematical problems, which are known to be insecure against quantum attacks. In this paper, we propose a certificateless signature scheme, based on NTRU lattice. The latter is based on the difficulty of small integer solutions on the NTRU lattice, and is known to be quantum attack resilience. Security analysis and performance evaluations demonstrate that our proposed scheme achieves significantly reduced communication and computation costs in comparison to two other competing quantum resilience schemes, while being quantum attack resilience.