PSDCE: Physiological signal-based double chaotic encryption for instantaneous E-healthcare services

Wang, Junchao; Huang, Dongmin; Fan, Shengwen; Han, Kaining; Jeon, Gwanggil; Rodrigues, Joel J. P. C.

doi:10.1016/j.future.2022.10.034

Cited by 12 publications

(4 citation statements)

References 35 publications

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“…For example, the expensive commercial diaphragm can be replaced with 3D-printed materials [ 73 ]. For signal transmission, the lung sound signal can be transmitted by matured technologies such as Bluetooth Low Energy [ 74 ] and Zigbee [ 75 ], allowing stethoscopes to be a part of the Internet of Medical Things to provide more comprehensive lung health assessments [ 76 ]. Furthermore, the development of wearable devices is also conducive to all-weather lung health monitoring.…”

Section: Digital Stethoscopesmentioning

confidence: 99%

Deep learning-based lung sound analysis for intelligent stethoscope

Huang,

Qiao

et al. 2023

Military Med Res

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Auscultation is crucial for the diagnosis of respiratory system diseases. However, traditional stethoscopes have inherent limitations, such as inter-listener variability and subjectivity, and they cannot record respiratory sounds for offline/retrospective diagnosis or remote prescriptions in telemedicine. The emergence of digital stethoscopes has overcome these limitations by allowing physicians to store and share respiratory sounds for consultation and education. On this basis, machine learning, particularly deep learning, enables the fully-automatic analysis of lung sounds that may pave the way for intelligent stethoscopes. This review thus aims to provide a comprehensive overview of deep learning algorithms used for lung sound analysis to emphasize the significance of artificial intelligence (AI) in this field. We focus on each component of deep learning-based lung sound analysis systems, including the task categories, public datasets, denoising methods, and, most importantly, existing deep learning methods, i.e., the state-of-the-art approaches to convert lung sounds into two-dimensional (2D) spectrograms and use convolutional neural networks for the end-to-end recognition of respiratory diseases or abnormal lung sounds. Additionally, this review highlights current challenges in this field, including the variety of devices, noise sensitivity, and poor interpretability of deep models. To address the poor reproducibility and variety of deep learning in this field, this review also provides a scalable and flexible open-source framework that aims to standardize the algorithmic workflow and provide a solid basis for replication and future extension: https://github.com/contactless-healthcare/Deep-Learning-for-Lung-Sound-Analysis.

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Section: Digital Stethoscopesmentioning

confidence: 99%

Deep learning-based lung sound analysis for intelligent stethoscope

Huang,

Qiao

et al. 2023

Military Med Res

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“…Wang et al [27] designed a physiological signal-based double chaotic encryption (PSDCE) for ehealthcare services. Electroencephalogram (EEG) signals are for the encryption process.…”

Section: Introductionmentioning

confidence: 99%

Securing and privacy preserving for electronic health records using two-phased scheme

Arulmozhi

P.D.S.

2023

Preprint

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Patient medical data are stored as Electronic Health Records (EHRs) in the cloud for decentralized clinical access. Information related to a patient’s health, diagnosis, and medication is vital for which individual privacy and security are vital considerations. This article introduces a Two-Phased Privacy Preserving Security Scheme (TP3SS) for EHR stored in clouds. The proposed scheme offers secure access control and attribute-based encryption for privacy-preserving and preventing data falsification. Secure access control is achieved by establishing mutual key-dependent smart contracts between the user, doctors and the EHR storage. The key authentication is provided using record-related attribute encryption that is valid within the contract period. The access and key validity are confined to the smart contract allocated interval by verifying the user identity. Here validity verification and access confinement are pursued using ledge-stored user information. The validation occurs for ensuring the EHR and user attributes are mapped together in the current and previous smart contract access sessions. In the record management process, Hyperledger fabric blockchain is used for preventing internal computation complexities. Similarly, the attribute that is inferred by the Hyperledger fabric blockchain in the current access session is alone used for a key generation; used for accessing and sharing the records. This process prevents the entry of adversary access and improves the security level under controlled complexity.

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“…In academic circles, interest in nonlinear science has grown, with chaos theorymarked by sensitivity to initial conditions and unpredictability in sequence generation [3,4]-emerging as a key feature of nonlinear systems. Owing to their extensive use in encryption [5][6][7][8][9][10][11][12][13][14][15], chaotic systems inspire us to develop a key extension algorithm that capitalizes on their security advantages. However, traditional one-dimensional chaotic systems are limited by issues such as discontinuous chaotic intervals and predictable behaviors [16,17].…”

mentioning

confidence: 99%

Advanced Key Expansion Algorithm Using a High-Performance Four-Dimensional Chaotic Map and a Strong S-Box for Enhanced Sensitivity and Independence

2024

Preprint

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The round key plays a critical role in the security of block encryption algorithms, being derived from the initial key through the key expansion process to bolster security. In this study, we introduce a novel key expansion algorithm powered by a high-performance four-dimensional chaotic map and a robust S-box, notable for its sensitivity and independence of keys, thereby strengthening defense against various cryptographic attacks. We present a four-dimensional chaotic map, celebrated for its outstanding dynamic properties as confirmed through detailed behavior analysis. Additionally, we propose an S-box generation technique based on operations in the finite field GF(28), resulting in an S-box with high nonlinearity and enhanced security. This S-box is carefully crafted to eliminate typical weaknesses such as fixed points, reverse fixed points, and short periods, making it suitable for key expansion applications. Utilizing the advantages of the chaotic map and S-box, we elaborate on our key expansion algorithm and demonstrate its security effectiveness through thorough evaluation, showcasing its promise as a potent instrument in cryptographic security.

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PSDCE: Physiological signal-based double chaotic encryption for instantaneous E-healthcare services

Cited by 12 publications

References 35 publications

Deep learning-based lung sound analysis for intelligent stethoscope

Deep learning-based lung sound analysis for intelligent stethoscope

Securing and privacy preserving for electronic health records using two-phased scheme

Advanced Key Expansion Algorithm Using a High-Performance Four-Dimensional Chaotic Map and a Strong S-Box for Enhanced Sensitivity and Independence

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