AI Authentication With UWB Proximity and Cryptographic Verification
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Solution Overview
Problem
Existing user authentication methods, particularly those using artificial intelligence, are prone to spoofing and false triggering, leading to security challenges and degraded user experience, especially in secure environments requiring minimal user interaction.
Innovation Solution
Implementing ultra-wideband (UWB) communication protocol with AI authorization for robust user authentication, utilizing real-time location data and cryptography to enhance security and accuracy, and obfuscate unique identifiers, combined with machine learning models trained on biometric and non-biometric inputs for user authorization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If AI authentication methods are used, then user experience is improved through automated recognition, but security is worsened due to susceptibility to spoofing and false triggering
Solution Approach 1:
The patent combines multiple authentication modalities including AI-based biometric recognition, UWB proximity verification, and cryptographic authentication into a unified multi-factor authentication system. This merging of different authentication layers maintains ease of use while significantly improving security by requiring multiple independent verification methods simultaneously
Solution Approach 2:
The authentication system uses composite verification methods combining artificial intelligence algorithms with physical layer security protocols (UWB) and cryptographic primitives. This composite approach creates a robust authentication mechanism that leverages the strengths of each component while mitigating their individual weaknesses
2Ease of operation
If minimal user interaction is implemented, then user convenience is improved, but authentication reliability is worsened due to increased false positives
Solution Approach 1:
The system introduces UWB-based proximity verification as an intermediary layer between the user and the authentication system. This intermediary provides a reliable physical presence confirmation that reduces false positives while maintaining minimal user interaction, as the UWB verification occurs automatically when the authenticated device is in proximity
3Reliability
If multiple authentication factors are required, then security is improved, but device complexity is worsened
Solution Approach 1:
The authentication system implements self-service mechanisms where the UWB module automatically performs proximity verification and the AI module automatically performs biometric analysis without requiring user configuration or intervention. The system autonomously manages the multiple authentication factors, reducing the perceived complexity for users while maintaining high security
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Provides secure and seamless user access to sensitive systems by ensuring high precision location verification and improved context for user credentials, reducing false positives and maintaining user privacy.
Implementation Method 1
Real time location data from UWB communications, such as Time of Flight (ToF) and Angle of Arrival (AoA), provides high precision
Implementation Method 2
Real time location data from UWB communications, such as Time of Flight (ToF) and Angle of Arrival (AoA), provides high precision
Data Source
AI summary
Secure AI authentication is implemented for selectable environments with a selectable combination of ML models processing selectable input credentials, e.g., biometric and/or non-biometric credentials, such as a key associated with a secure model, user location information, a user gesture credential, and/or a user movement pattern credential. ML models may be selectively applied in serial or parallel in a selected authorization procedure. ML model applicability may vary based on one or more parameters, such as time of day, or one or more detected input credentials, such as user gestures, secure model keys, or biometric voice or face recognition. For example, AI authorization (e.g., for biometric credentials) augmented with an ultra-wideband (UWB) communication protocol provides robust user authentication via a native cryptographic exchange and accurate user location credentials for proximity and geo-fenced confirmation of other user credentials, such as biometric credentials, thereby preventing false positives by spoofing.