Biometric Authentication for Secure Remote IMD Programming
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Solution Overview
Problem
Existing implantable medical devices face security vulnerabilities in remote patient care due to legacy authentication methods like user names and passwords, which are susceptible to phishing and brute force attacks, compromising the integrity of therapy programming sessions.
Innovation Solution
Implement biometric authentication systems using facial images, voice inputs, geofencing, and access point data, combined with machine learning algorithms to dynamically adjust challenge complexity, ensuring secure remote programming of implantable medical devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If biometric authentication systems are implemented, then security and integrity of remote therapy sessions are enhanced, but device complexity and authentication process time increase
Solution Approach 1:
The authentication system is segmented into multiple independent components: biometric data collection module, machine learning analysis module, challenge query generation module, and decision module. Each component performs a specific function, allowing the system to be developed, tested, and maintained independently while providing comprehensive security through coordinated operation of these modules.
Solution Approach 2:
The system performs preliminary actions by collecting and processing biometric data (facial images, voice inputs) and determining contextual information (geofence, access point data) before the authentication decision is made. Challenge queries are generated and presented to users in advance based on preliminary risk assessment, allowing the system to prepare authentication challenges proactively rather than reactively.
2Reliability
If dynamic challenge complexity adjustment is implemented, then authentication security is improved, but processing time and computational requirements increase
Solution Approach 1:
The challenge query complexity is made dynamic rather than static. The system adjusts the complexity level of challenge queries in real-time based on the user's biometric verification scores and contextual factors such as geofence location and access point data. This allows the system to present simpler challenges to trusted users in familiar contexts while imposing more complex challenges when risk is detected, optimizing both security and user experience.
Solution Approach 2:
The system changes parameters of the authentication challenge based on input data quality and risk assessment. When biometric scores are high and contextual factors are favorable, the system reduces challenge complexity parameters. When scores are low or contextual factors indicate potential risk, the system increases challenge complexity parameters, thereby adapting the authentication burden to the actual security risk level.
Data Source
AI summary
A digital healthcare architecture including a cloud-based virtual clinic platform configured to facilitate remote therapy of one or more patients based on biometric authentication systems and methods. In an example arrangement, one or more biometric indicia of a user (e.g., a clinician and/or a patient) as well as one or more non-biometric input factors may be used in authenticating the user prior to granting access to a protected resource or application (e.g., a therapy application executing on a UE device) configured for effectuating remote programming of an implantable medical device (IMD) operative to provide therapy to the patient.