Smart Health Care for Societies: An Insight into the Implantable and Wearable Devices for Remote Health Monitoring

Ghosh, Jeet; Samanta, G. P.; Chakraborty, Chinmay

doi:10.1007/978-3-030-73295-0_5

Cited by 5 publications

(6 citation statements)

References 59 publications

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“…[48,49,50] Vulnerabilities in the user interface, network services, sensors, cloud services, and confidential threats. [51,52,53] At the time of data sharing, vulnerabilities are found in software and hardware.…”

Section: Internet Of Medical Things (Iomt)mentioning

confidence: 99%

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Security and Privacy of Wearable Wireless Sensors in Healthcare: A Systematic Review

Kaur,

Shahrestani,

Ruan

2024

Comput. Networks Commun.

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Wearable Wireless Sensor Network (WWSN) devices are widely used in healthcare to monitor health data. However, when WWSN users transmit their data to healthcare professionals or third parties over wireless connections, they face privacy and security vulnerabilities. This paper aims to identify the unsolved privacy and security challenges in wearable sensor devices in healthcare, especially the aspects overlooked by previous research. The main research question is: What are the unsolved privacy and security challenges in wearable sensor devices in healthcare, and what are their implications for users and healthcare professionals? This systematic review employs specific keywords to search for relevant publications on bibliographic databases, including Google Scholar, Scopus, IEEE Xplore, and Web of Science. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) charts helped in screening and summarising the selected papers. The results highlight the critical areas that can make WWSNs vulnerable to security attacks. The findings examine the security and privacy issues of wearable sensor devices in cloud computing, fog computing, the Internet of Things (IoT) and the like. Many studies examine IoT due to its privacy and security challenges, especially regarding handling extensive data, using public channels, deploying advanced technologies, managing sharing policies alongside the growing number of wireless devices, and protecting data from hackers. These challenges seriously threaten the confidentiality, integrity, and availability of health data transmitted by WWSN users to healthcare professionals or third parties in cloud-based environments and IoT and are exacerbated by limited resources. The significant findings thereby focus on unresolved areas in IoT. This paper aims to safeguard against cyber-attacks on healthcare and increase users' adoption rate of WWSN devices.

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Section: Internet Of Medical Things (Iomt)mentioning

confidence: 99%

“…It is said that when a wearable sensor or an implanted sensor is remotely connected to several machines, malicious assaults can occur as a result of the undesired connections made by the sensors [55]. Transferring data from sensors to the cloud via IoT can raise security concerns [53].…”

Section: Weak Authentication Systems In Wireless Technologymentioning

confidence: 99%

Security and Privacy of Wearable Wireless Sensors in Healthcare: A Systematic Review

Kaur,

Shahrestani,

Ruan

2024

Comput. Networks Commun.

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“…This approach uses a two-stage LDA process, where each client has an associated multinomial distribution over the coarse labels of CIFAR-100, and a coarse-to-fine label multinomial distribution for that coarse label over the labels under that coarse label. 1 https://github.com/besherh/FedNets…”

Section: ) Federated Cifar100 For Simulationmentioning

confidence: 99%

“…W Ith the proliferation of the Internet of Things (IoT), and the launch of 5G networks, the IoT has emerged as one of the major technological advances in our lives. We can see their advances in different domains, including wearable smart health devices [1], intelligent energy networks, smart transportation [2] and smart building [3] [4]. These tiny connected devices generate massive amounts of data on the network edge, giving great opportunities to generate valuable insights and complete sophisticated machine learning (ML) tasks.…”

Section: Introductionmentioning

confidence: 99%

FedNets: Federated Learning on Edge Devices Using Ensembles of Pruned Deep Neural Networks

2023

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Federated Learning (FL) is an innovative area of machine learning that enables different clients to collaboratively generate a shared model while preserving their data privacy. In a typical FL setting, a central model is updated by aggregating the clients' parameters of the respective artificial neural network. The aggregated parameters are then sent back to the clients. However, two main challenges are associated with the central aggregation approach. Firstly, most state-of-the-art strategies are not optimised to operate in the presence of certain types of non-iid (not independent and identically distributed) applications and datasets. Secondly, federated learning is vulnerable to various privacy and security concerns related to model inversion attacks, which can be used to access sensitive information from the training data.To address these issues, we propose a novel federated learning strategy FedNets based on ensemble learning. Instead of sharing the parameters of the clients over the network to update a single global model, our approach allows clients to have ensembles of diverse-lightweight models and collaborate by sharing ensemble members. FedNets utilises graph embedding theory to reduce the complexity of running Deep Neural Networks (DNNs) on resource-limited devices. Each Deep Neural Network (DNN) is treated as a graph, from which respective graph embeddings are generated and clustered to determine which part of the DNN should be shared with other clients. Our approach outperforms state-of-the-art FL algorithms such as Federated Averaging (Fed-Avg) and Adaptive Federated Optimisation (Fed-Yogi) in terms of accuracy; on the Federated CIFAR100 dataset (non-iid), FedNets demonstrates a remarkable 63% and 92% improvement in accuracy, respectively. Furthermore, FedNets does not compromise the client's privacy, as it is safeguarded by the design of the method.

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“…However, the popularity of using various body-worn IoT devices has increased the risk of confidentiality and integrity loss which is risky for the social well-being of the patient [1,2]. To overcome such challenges, fully homomorphic encryption (FHE) is introduced in the medical healthcare context to provide secured healthcare services for improved healthcare service quality and contribution towards society [3].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Autonomous Protocol for Secured Remote Healthcare Using Fully Homomorphic Encryption (AutoPro-RHC)

Sheu,

Lin,

Pardeshi

et al. 2023

Sensors

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The outreach of healthcare services is a challenge to remote areas with affected populations. Fortunately, remote health monitoring (RHM) has improved the hospital service quality and has proved its sustainable growth. However, the absence of security may breach the health insurance portability and accountability act (HIPAA), which has an exclusive set of rules for the privacy of medical data. Therefore, the goal of this work is to design and implement the adaptive Autonomous Protocol (AutoPro) on the patient’s remote healthcare (RHC) monitoring data for the hospital using fully homomorphic encryption (FHE). The aim is to perform adaptive autonomous FHE computations on recent RHM data for providing health status reporting and maintaining the confidentiality of every patient. The autonomous protocol works independently within the group of prime hospital servers without the dependency on the third-party system. The adaptiveness of the protocol modes is based on the patient’s affected level of slight, medium, and severe cases. Related applications are given as glucose monitoring for diabetes, digital blood pressure for stroke, pulse oximeter for COVID-19, electrocardiogram (ECG) for cardiac arrest, etc. The design for this work consists of an autonomous protocol, hospital servers combining multiple prime/local hospitals, and an algorithm based on fast fully homomorphic encryption over the torus (TFHE) library with a ring-variant by the Gentry, Sahai, and Waters (GSW) scheme. The concrete-ML model used within this work is trained using an open heart disease dataset from the UCI machine learning repository. Preprocessing is performed to recover the lost and incomplete data in the dataset. The concrete-ML model is evaluated both on the workstation and cloud server. Also, the FHE protocol is implemented on the AWS cloud network with performance details. The advantages entail providing confidentiality to the patient’s data/report while saving the travel and waiting time for the hospital services. The patient’s data will be completely confidential and can receive emergency services immediately. The FHE results show that the highest accuracy is achieved by support vector classification (SVC) of 88% and linear regression (LR) of 86% with the area under curve (AUC) of 91% and 90%, respectively. Ultimately, the FHE-based protocol presents a novel system that is successfully demonstrated on the cloud network.

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Smart Health Care for Societies: An Insight into the Implantable and Wearable Devices for Remote Health Monitoring

Cited by 5 publications

References 59 publications

Security and Privacy of Wearable Wireless Sensors in Healthcare: A Systematic Review

Security and Privacy of Wearable Wireless Sensors in Healthcare: A Systematic Review

FedNets: Federated Learning on Edge Devices Using Ensembles of Pruned Deep Neural Networks

Adaptive Autonomous Protocol for Secured Remote Healthcare Using Fully Homomorphic Encryption (AutoPro-RHC)

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