Adaptive Autonomous Protocol for Secured Remote Healthcare Using Fully Homomorphic Encryption (AutoPro-RHC)
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/20/8504 |
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| author | Ruey-Kai Sheu Yuan-Cheng Lin Mayuresh Sunil Pardeshi Chin-Yin Huang Kai-Chih Pai Lun-Chi Chen Chien-Chung Huang |
| author_facet | Ruey-Kai Sheu Yuan-Cheng Lin Mayuresh Sunil Pardeshi Chin-Yin Huang Kai-Chih Pai Lun-Chi Chen Chien-Chung Huang |
| author_sort | Ruey-Kai Sheu |
| collection | DOAJ |
| description | 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 <b>r</b>emote <b>h</b>ealth<b>c</b>are (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. |
| first_indexed | 2024-03-10T20:54:24Z |
| format | Article |
| id | doaj.art-17f8c6e1eb2d4b67bf2aeec4945fbc7e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T20:54:24Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-17f8c6e1eb2d4b67bf2aeec4945fbc7e2023-11-19T18:03:54ZengMDPI AGSensors1424-82202023-10-012320850410.3390/s23208504Adaptive Autonomous Protocol for Secured Remote Healthcare Using Fully Homomorphic Encryption (AutoPro-RHC)Ruey-Kai Sheu0Yuan-Cheng Lin1Mayuresh Sunil Pardeshi2Chin-Yin Huang3Kai-Chih Pai4Lun-Chi Chen5Chien-Chung Huang6Department of Computer Science, Tunghai University, Taichung 407224, TaiwanDepartment of Industrial Engineering and Enterprise Information, Tunghai University, Taichung 407224, TaiwanAI Center, Tunghai University, Taichung 407224, TaiwanDepartment of Industrial Engineering and Enterprise Information, Tunghai University, Taichung 407224, TaiwanCollege of Engineering, Tunghai University, Taichung 407224, TaiwanCollege of Engineering, Tunghai University, Taichung 407224, TaiwanDepartment of Computer Science, Tunghai University, Taichung 407224, TaiwanThe 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 <b>r</b>emote <b>h</b>ealth<b>c</b>are (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.https://www.mdpi.com/1424-8220/23/20/8504remote healthcare (RHC)federated protocolfully-homomorphic encryptioncloud computingheart diseases |
| spellingShingle | Ruey-Kai Sheu Yuan-Cheng Lin Mayuresh Sunil Pardeshi Chin-Yin Huang Kai-Chih Pai Lun-Chi Chen Chien-Chung Huang Adaptive Autonomous Protocol for Secured Remote Healthcare Using Fully Homomorphic Encryption (AutoPro-RHC) Sensors remote healthcare (RHC) federated protocol fully-homomorphic encryption cloud computing heart diseases |
| title | Adaptive Autonomous Protocol for Secured Remote Healthcare Using Fully Homomorphic Encryption (AutoPro-RHC) |
| title_full | Adaptive Autonomous Protocol for Secured Remote Healthcare Using Fully Homomorphic Encryption (AutoPro-RHC) |
| title_fullStr | Adaptive Autonomous Protocol for Secured Remote Healthcare Using Fully Homomorphic Encryption (AutoPro-RHC) |
| title_full_unstemmed | Adaptive Autonomous Protocol for Secured Remote Healthcare Using Fully Homomorphic Encryption (AutoPro-RHC) |
| title_short | Adaptive Autonomous Protocol for Secured Remote Healthcare Using Fully Homomorphic Encryption (AutoPro-RHC) |
| title_sort | adaptive autonomous protocol for secured remote healthcare using fully homomorphic encryption autopro rhc |
| topic | remote healthcare (RHC) federated protocol fully-homomorphic encryption cloud computing heart diseases |
| url | https://www.mdpi.com/1424-8220/23/20/8504 |
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