ML Access Control with Quantum-Offloaded Model Training
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Solution Overview
Problem
Conventional access control systems face challenges in providing personalized permission settings for user devices in a network without overconsumption of processing resources, leading to degraded system performance and vulnerability to data leakage and malicious attacks.
Innovation Solution
An access control system that offloads machine learning model training to a quantum computing device for load balancing, allowing dynamic access control decisions based on user and document content, using machine learning models trained locally or remotely to optimize resource utilization and improve security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If personalized permission settings are provided for each device, then information security is improved, but processing resource consumption increases significantly
Solution Approach 1:
The patent segments the access control system into multiple components: a machine learning model training module that creates permission models, an access control module that enforces permissions, and a machine learning inference module that evaluates access requests. This segmentation allows personalized security decisions to be made efficiently without requiring full processing resources for each decision.
Solution Approach 2:
The system performs preliminary action by training machine learning models in advance to create permission settings. The access control module uses pre-trained models to quickly evaluate access requests without requiring intensive processing resources at the time of each access decision, thus providing personalized security efficiently.
2Adaptability or versatility
If permission settings are frequently adjusted, then adaptability to new threats is improved, but system performance degrades due to resource strain
Solution Approach 1:
The patent implements dynamic access control by continuously training machine learning models with new data and updating permission settings adaptively. The system can adjust permissions in response to new threats while using efficient inference mechanisms that maintain system performance during these adjustments.
Solution Approach 2:
The system incorporates feedback mechanisms where access decisions and security events are fed back into the machine learning model training process. This allows the system to learn from actual security events and adjust permissions dynamically while maintaining performance through efficient model updates rather than complete retraining.
3Reliability
If delayed response to attacks is prevented, then information security is improved, but processing resources are overconsumed by constant monitoring
Solution Approach 1:
The system performs preliminary action by training machine learning models in advance to recognize attack patterns and anomalies. When security events occur, the pre-trained models can quickly evaluate whether an attack is underway without requiring intensive real-time processing, thus providing rapid threat detection with minimal resource consumption.
Data Source
AI summary
A device configured to receive training data that includes user information for a plurality of users and a set of data identifiers for a plurality of data elements. The device is further configured to determine a data size for the training data is greater than a predetermined data size threshold value, and in response, send the training data to a quantum computing device. The quantum computing device is configured to train a first machine learning model using the training data. The device is further configured to receive a set of machine learning model parameters comprising a set of weight coefficients from the quantum computing device in response to training the first machine learning model and to configure a second machine learning model using the set of machine learning model parameters.