Robot ML Model Permission Control With Secure Server Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional robot control systems lack effective management and security measures for training data and machine learning models, leading to risks of unauthorized copying and use, resulting in potential business losses.
Innovation Solution
A machine learning model operation management system comprising a model building server, an operation server, and a management server, where the model building server builds and encrypts the machine learning model, and the operation server manages permissions and operation information, ensuring secure use and reducing the need for strict security measures in the operation server's location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If training data and learning model data are stored and managed without permission control, then the system is simple and easy to operate, but security risks increase due to unauthorized copying and use
Solution Approach 1:
The system divides the management functions into two separate servers: a model building server that handles training data and model creation, and an operation server that manages permission control and deployment. This segmentation allows security-critical functions to be isolated while maintaining overall system simplicity.
Solution Approach 2:
The system performs permission verification in advance before allowing the robot controller to use the learning model. The operation server checks whether the robot controller has permission to use the model before execution, preventing unauthorized use without requiring complex security infrastructure.
2Reliability
If strict security measures are implemented in the operation server location, then security of training data and models is improved, but construction and running costs increase
Solution Approach 1:
The system extracts the security-critical model building function to a separate model building server, allowing the operation server to operate in a less secure, more cost-effective location. Only permission control data is stored in the operation server, not the actual training data or models.
Solution Approach 2:
The model building server acts as an intermediary that handles all interactions involving training data and model creation. The operation server only manages permission control, reducing its security requirements while maintaining overall system security through the intermediary architecture.
3Reliability
If the robot controller continuously checks permission before using the learning model, then security is maintained, but operation time and processing overhead increase
Solution Approach 1:
The system performs permission verification in advance before the robot controller begins using the learning model. Once permission is granted, the robot controller can use the model without continuous checks, reducing operational overhead while maintaining security.
Data Source
AI summary
A machine learning model operation management system includes a model building server and an operation server. The model building server builds a trained machine learning model based on received training data. When the trained machine learning model stored in a robot controller operates for determining the operation of a robot, the operation server receives operation information generated by the robot controller. Data of the trained machine learning model built by the model building server is assigned with model identification information that uniquely identifies the trained machine learning model. The robot controller makes an external inquiry as to whether or not it has permission to use the trained machine learning model stored in itself, and if it has the permission to use, it makes the trained machine learning model available for use. The operation server stores the operation information in association with the model identification information.