Brain-Machine Interface Authentication Using Hardware Identifiers
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Solution Overview
Problem
Existing brain-machine interface (BMI) authentication methods are insecure and inefficient, relying on single-factor or two-factor authorization that introduces latency and compromises security.
Innovation Solution
A multi-factor authentication system for BMIs that uses secret information, hardware component identifiers, and biometric properties to authenticate users without increasing latency or memory usage on the BMI.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multi-factor authentication is implemented using secret information, hardware identifiers, and biometric data, then security and authentication reliability are improved, but system complexity and processing requirements increase
Solution Approach 1:
The authentication system is segmented into multiple independent factors (secret information, hardware identifiers, biometric data) that can be processed separately. Each factor is evaluated independently by different components of the system, allowing complex authentication to be broken down into manageable segments that reduce overall system complexity while maintaining high security standards
2Reliability
If traditional two-factor authentication is used with latency introduction, then security is improved, but authentication speed and user experience deteriorate
Solution Approach 1:
Biometric data and hardware identifiers are pre-configured and stored in the system before authentication is needed. During the authentication process, these pre-prepared elements can be quickly compared against incoming data without requiring real-time computation or external verification, significantly reducing authentication latency while maintaining security
Solution Approach 2:
The system performs self-verification of authentication factors by comparing biometric data and hardware identifiers against stored reference data without requiring external authentication services. This self-contained verification process eliminates network delays and external dependency, reducing authentication time while maintaining security standards
Data Source
AI summary
In some implementations, a front-end device may receive, from a brain-machine interface (BMI) associated with a user, a request to authenticate the user with secret information associated with the user. Accordingly, the front-end device may transmit, to the BMI, a request for an identifier associated with one or more hardware components of the BMI. The front-end device may receive, from the BMI, an indication of the identifier associated with the one or more hardware components. Accordingly, the front-end device may authenticate the user based on the secret information associated with the user and the identifier associated with the one or more hardware components. Additionally, or alternatively, the front-end device may authenticate the user based on a location of an external device associated with the user and/or an indication of a biometric property associated with the user.