Authorized ML Model Retrieval via Access Tokens in 5G Networks
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Solution Overview
Problem
Current communication systems face inefficiencies and security issues in network data analytics services, particularly in the retrieval of machine learning models, as they lack defined authorization schemas and cannot verify the authorization of consumers, leading to inefficient data delivery and large model transfer issues.
Innovation Solution
Implement an authorization schema using protocols like OAuth 2.0, incorporating model identifiers into access tokens to authorize network function service consumers, enabling secure retrieval of machine learning models from Analytics Data Repository Functions or network function producers, and optimizing model transfer via standardized protocols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If network data analytics services are provided without authorization schemas, then service flexibility is improved, but security deteriorates
Solution Approach 1:
The patent introduces an authorization schema as an intermediary layer between service consumers and machine learning models. This schema includes authorization rules that verify consumer credentials and model accessibility, enabling secure access control without restricting service flexibility. The authorization schema acts as a mediator that validates requests while maintaining the Service Based Architecture's inherent flexibility.
2Speed
If machine learning models are transferred without authorization verification, then data delivery speed is improved, but security deteriorates
Solution Approach 1:
The patent implements preliminary authorization verification through authorization rules that are evaluated before machine learning model transfer occurs. The service consumer must present credentials and the system must verify accessibility rights before initiating model transfer, ensuring security is established in advance without blocking legitimate fast data delivery.
3Reliability
If authorization schemas are implemented for model retrieval, then security is improved, but device complexity deteriorates
Solution Approach 1:
The patent designs the authorization schema to be universally applicable across multiple network functions and service consumers within the 5G architecture. The same authorization rules and verification mechanisms are reused for different machine learning models and consumer types, reducing overall system complexity through standardization rather than creating separate authorization systems for each case.
4Reliability
If comprehensive authorization verification is performed, then security is improved, but productivity deteriorates
Solution Approach 1:
The patent implements partial authorization verification by evaluating only the necessary authorization rules required for each specific model retrieval request. The system assesses credentials and accessibility rights based on the particular service consumer and model combination, avoiding unnecessary verification steps while maintaining adequate security for each transaction.
Data Source
AI summary
Methods, systems, apparatuses, and computer program products are provided for authorized machine learning model retrieval for a communications network. In this regard, an access token request for one or more machine learning models related to a communications network is received from a network function service consumer (NFc). The access token request includes information to identify the one or more machine learning models. The NFc is then authorized with respect to the one or more machine learning models based on the information included in the access token request. Additionally, enhanced an access token for retrieving the one or more machine learning models is provided to the NFc based on valid authorization of the NFc with respect to the one or more machine learning models.