Distributed Biometric Profiling for Low-Latency 5G Authentication
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Solution Overview
Problem
Existing biometric data systems face challenges with data siloing, lack of comprehensive data access, and dynamic variability, leading to inefficiencies and potential security vulnerabilities, especially in 5G networks, which hinder seamless authentication across different devices and geographic locations.
Innovation Solution
A 5G NR-based network architecture with a distributed biometric database model and intelligent biometric-enabled CU and DU units, enabling dynamic and agile biometric data management and persistence across multiple devices and regions, using algorithms to verify user identity and secure data access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If biometric data is stored in distributed databases across multiple devices and locations, then data accessibility and authentication capability are improved, but data security and risk of unauthorized access worsen
Solution Approach 1:
The biometric database is segmented into multiple distributed nodes across different devices and locations. Each node stores portions of biometric data, allowing the system to maintain data accessibility while reducing the security risk at any single location. The segmentation enables the system to query multiple nodes for authentication without requiring all data to be accessible from one central location.
Solution Approach 2:
The patent introduces intermediary components including trusted execution environments (TEEs) and secure enclaves that act as mediators between authentication requests and biometric data storage. These intermediaries verify authentication credentials without exposing the actual biometric data, thereby maintaining security while enabling accessibility. The intermediary layer prevents direct access to raw biometric data while still allowing authentication operations.
2Measurement precision
If biometric data is dynamically updated and maintained across distributed systems, then authentication accuracy and user profile completeness are improved, but data consistency and synchronization complexity worsen
Solution Approach 1:
The system implements feedback mechanisms where authentication results and data usage patterns are continuously monitored and fed back to update the biometric profiles. This feedback loop enables the system to improve authentication accuracy over time by learning from actual usage while automatically managing data synchronization across distributed nodes. The feedback mechanism includes verifying authentication outcomes and adjusting data consistency protocols based on observed patterns.
Solution Approach 2:
The patent employs preliminary actions by pre-establishing synchronization protocols and data consistency rules before distribution issues arise. Data is pre-processed and validated before being distributed to multiple nodes, reducing the complexity of ongoing synchronization. The system performs preliminary consistency checks and establishes replication strategies in advance, so that when updates occur, the synchronization process is already optimized and less complex.
3Reliability
If multiple biometric parameters are collected and analyzed, then identification reliability and security are improved, but data processing time and computational requirements worsen
Solution Approach 1:
The system applies partial action by selectively analyzing only the necessary subset of biometric parameters for each authentication context rather than processing all available parameters. The patent determines which biometric modalities are most relevant based on the specific authentication scenario, user profile, and security requirements, thereby reducing processing time while maintaining identification reliability. This selective approach avoids the excessive processing of unnecessary biometric data.
Solution Approach 2:
The patent implements dynamic parameter selection where the set of biometric parameters to be analyzed is adjusted in real-time based on contextual factors such as authentication risk level, available computational resources, and user preferences. The system dynamically determines the optimal combination of biometric parameters to process, balancing identification reliability with processing time requirements. This dynamic approach allows the system to use fewer parameters when speed is critical and more parameters when maximum reliability is needed.
Data Source
AI summary
Methods and apparatus for biometric data maintenance, access and distribution across two or more experiential and/or network domains. In one embodiment, a 5G NR-based network architecture is provided which allows ultra-low latency and effectively user-imperceptible biometric data use for e.g., authentication and maintenance of user state across multiple domains via multiple constituent user devices (e.g., UEs). The network architecture includes both (i) a distributed biometric database (BDB) model wherein relevant biometric data for individuals/UEs is intelligently cached in various portions of the distributed database, and (ii) centralized and local BAEs (biometric analytics entities) which manage the aforementioned intelligent caching, as well as network configuration using one or both of 5G NR network “slicing” and CU/DU split options to optimize end-user biometric-related applications such as those providing identification/authentication, AR functions, VR functions or yet others.