A Secure and Authentication Scheme to Preserve the Privacy of Electronic Health Records in the Healthcare System

Hamed, Naba M.; Yassin, Ali A.

doi:10.1109/iiccit55816.2022.10010500

Cited by 3 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the United States (U.S.), all participants within the healthcare ecosystem are mandated to uphold the security and safety regulations of the Health Insurance Portability and Accountability Act (HIPAA) during the collection, storage, and exchange of EHRs [8,9]. However, the task of maintaining system security and safeguarding privacy presents an ongoing challenge [10][11][12]. Authentication involves verifying an individual's identity to either allow or deny access to services in a secure system.…”

Section: Introductionmentioning

confidence: 99%

ResNet1D-Based Personal Identification with Multi-Session Surface Electromyography for Electronic Health Record Integration

Ganiga,

S. N.,

Choi

et al. 2024

Sensors

View full text Add to dashboard Cite

Personal identification is an important aspect of managing electronic health records (EHRs), ensuring secure access to patient information, and maintaining patient privacy. Traditionally, biometric, signature, username/password, photo identity, etc., are employed for user authentication. However, these methods can be prone to security breaches, identity theft, and user inconvenience. The security of personal information is of paramount importance, particularly in the context of EHR. To address this, our study leverages ResNet1D, a deep learning architecture, to analyze surface electromyography (sEMG) signals for robust identification purposes. The proposed ResNet1D-based personal identification approach using the sEMG signal can offer an alternative and potentially more secure method for personal identification in EHR systems. We collected a multi-session sEMG signal database from individuals, focusing on hand gestures. The ResNet1D model was trained using this database to learn discriminative features for both gesture and personal identification tasks. For personal identification, the model validated an individual’s identity by comparing captured features with their own stored templates in the healthcare EHR system, allowing secure access to sensitive medical information. Data were obtained in two channels when each of the 200 subjects performed 12 motions. There were three sessions, and each motion was repeated 10 times with time intervals of a day or longer between each session. Experiments were conducted on a dataset of 20 randomly sampled subjects out of 200 subjects in the database, achieving exceptional identification accuracy. The experiment was conducted separately for 5, 10, 15, and 20 subjects using the ResNet1D model of a deep neural network, achieving accuracy rates of 97%, 96%, 87%, and 82%, respectively. The proposed model can be integrated with healthcare EHR systems to enable secure and reliable personal identification and the safeguarding of patient information.

show abstract

Section: Introductionmentioning

confidence: 99%

ResNet1D-Based Personal Identification with Multi-Session Surface Electromyography for Electronic Health Record Integration

Ganiga,

S. N.,

Choi

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

Multi-Factor Authentication for Secured Access Control of Electronic Health Records in South African Public Hospitals (Preprint)

Chuma Jr,

Msomi Jr

2024

Journal of Medical Internet Research

View full text Add to dashboard Cite

Multi-Factor Authentication for Secured Access Control to Electronic Health Records in South African Public Hospitals (Preprint)

Chuma Jr,

Msomi Jr

2024

Preprint

View full text Add to dashboard Cite

BACKGROUND In this rapidly advancing digital landscape, the security of Electronic Health Records (EHRs) is increasingly dependent on robust authentication and access control measures. Despite advancements in cybersecurity, South African public hospitals are particularly vulnerable and targeted by cyber-attacks and data breaches due to vulnerabilities associated with username and password-based authentication. These vulnerabilities pose substantial risks to the security and privacy of EHRs and cause huge disruptions to public hospitals. OBJECTIVE With the potential to cause widespread disruption and harm, this study aims to propose a framework for integrating Multi-Factor Authentication (MFA) to enhance to user authentication and access control to EHRs in South African public hospitals. METHODS A qualitative research design was employed to understand security vulnerabilities and risks in password authentication within public hospitals. The study conducted semi-structured interviews with 15 purposively selected IT technicians, network controllers, and IT managers working in public hospitals. All interviews were audio-recorded, transcribed verbatim, and analyzed using thematic analysis and NVivo version 12. The study applied a conceptual framework grounded in Protection Motivation Theory. RESULTS The analysis revealed that public hospitals experienced authentication vulnerabilities such as username enumeration, broken authentication, weak credentials, and credential leakage. Phishing, cryptojacking, ransomware, and password attacks were among the security incidents encountered in public hospitals. Participants expressed that security vulnerabilities in hospitals are due to weak and easily guessable passwords created by staff, the reuse of the same password across multiple systems, irregular password updates, reliance on legacy systems, writing down passwords on paper, and the lack of regular updates to Windows Firewall and Microsoft Defender Antivirus. CONCLUSIONS The study emphasizes the need for developing robust password policies, modernizing legacy systems, and promoting cybersecurity awareness training in public hospitals. Furthermore, the study suggested a framework for public hospitals to effectively address authentication vulnerabilities. and reinforcing data security. The research underscores that, despite the ongoing vulnerabilities and weaknesses of password and username-based authentication, the study concludes that the integration of MFA offers a scalable solution to significantly improve the security and access control of EHRs in public hospitals.

show abstract

A Secure and Authentication Scheme to Preserve the Privacy of Electronic Health Records in the Healthcare System

Cited by 3 publications

References 20 publications

ResNet1D-Based Personal Identification with Multi-Session Surface Electromyography for Electronic Health Record Integration

ResNet1D-Based Personal Identification with Multi-Session Surface Electromyography for Electronic Health Record Integration

Multi-Factor Authentication for Secured Access Control of Electronic Health Records in South African Public Hospitals (Preprint)

Multi-Factor Authentication for Secured Access Control to Electronic Health Records in South African Public Hospitals (Preprint)

Contact Info

Product

Resources

About