Mohamed Saddek Derki’s research while affiliated with Research Center on Scientific and Technical Information and other places

Pervasive Health Monitoring (PHM) uses sensors and wearable devices and data analytics for real-time health monitoring. It enables early detection and personalized care interventions. This technology has the potential to revolutionize healthcare by improving proactive and preventive care. Besides, Deep learning (DL) based PHM is even more promising as it improves the discovery of complex patterns and correlations. This leads to precise health monitoring and personalized care, enhances diagnostics, and ultimately improves patient outcomes in the field of healthcare. However, privacy and security considerations must be addressed for successful implementation. This paper investigates the security and privacy concerns in Pervasive Health Monitoring architectures. It discusses through an illustrative DL-based PHM architecture the potential threats and attacks during the inference and training phases, and identifies key security and privacy issues. It also gives insights on countermeasures and technological solutions that can address security and privacy concerns in PHM architectures.

We review in this report more than fifty research papers, from which we draw a taxonomy of design criteria, and sketch a novel architecture for pervasive health monitoring.

Fog computing becomes an essential part in IoT-based environment, it offers computing and storage resources at the edge of the network making applications and services more efficient. However, it belongs to a system loaded with security challenges and it is expected to help with its characteristics. On the other side, attribute based encryption is a rising solution that offers a fine-grained access control over encrypted data. Nonetheless, the calculation costs remains heavy and hard to be performed especially in a constrained environment. In this paper, we propose an access control scheme based on cipher-text policy attribute based encryption (CP-ABE) in an IoT- fog based environment by outsourcing the bulk of the calculation to the fog computing layer after a symmetric encryption performed by the devices. Moreover, we propose a verifiability mechanism and an attribute based key management scheme to maintain a fine-grained access control. The tests show that compared to other outsourcing schemes, the execution times on the IoT devices in our work are shorter and tolerable.

<<< Full-text access through Share Link from Elsevier (accessible until December 26, 2021) https://authors.elsevier.com/a/1e1dD_rVZSljRB >>>> The continuous growth of an aging population in some countries, and patients with chronic conditions needs the development of efficient solutions for healthcare. Pervasive Health Monitoring (PHM) is an important pervasive computing application that has the potential to provide patients with a high-quality medical service and enable quick-response alerting of critical conditions. To that end, PHM enables continuous and ubiquitous monitoring of patients' health and wellbeing using Internet of Things (IoT) technologies, such as wearables and ambient sensors. In recent years, deep learning (DL) has attracted a growing interest from the research community to improve PHM applications. In this paper, we discuss the state-of-the-art of DL-based PHM, through identifying, (1) the main PHM applications where DL is successful, (2) design goals and objectives of using DL in PHM, and (3) design notes including DL architectures and data preprocessing. Finally, main advantages, limitations and challenges of the adoption of DL in PHM are discussed.

In this report, we explore the use of deep learning (DL) in the domain of Pervasive Health Monitoring (PHM) through reviewing a number of DL-based PHM works, from which we identify (i) design goals and objectives of adopting DL in PHM, (ii) interesting design notes, including DL architectures and data preprocessing, and (iii) Main advantages and limitations of the adoption of DL in PHM.