Security-Aware LLM Training via Permission-Based Corpus Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Machine learning models, particularly large language models, face challenges in access control, as they often use extensive training data from various sources with different access requirements, leading to risks of unauthorized data access and potential data leaks.
Innovation Solution
The method involves segmenting the training corpus based on model permission requirements, training machine learning models using the segmented data, and associating the trained models with these requirements to ensure that only authorized users can access the data, thereby preventing unauthorized access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire training corpus is used for training machine learning models, then the model's learning capability and effectiveness are improved, but the risk of unauthorized data access and data leaks increases
Solution Approach 1:
The training corpus is segmented into multiple subsets based on permission requirements. Each subset contains data that can be accessed by users with specific permission levels. The system divides the entire corpus into permission-based segments, allowing models to be trained on appropriate portions without exposing sensitive data to unauthorized users.
Solution Approach 2:
Different segments of the training corpus are assigned different permission levels and access controls. Each segment has its own quality characteristics based on sensitivity and access requirements. Users with higher permission levels can access more segments, while users with lower permission levels access only appropriate segments.
2Adaptability or versatility
If multiple data sources with different access requirements are combined for training, then the model's comprehensiveness is improved, but the complexity of access control management increases
Solution Approach 1:
The system implements a universal permission framework that works across multiple data sources and model types. A single permission requirement structure can be applied to diverse training data, and the same framework manages access to multiple generated models, reducing the need for separate access control systems for each data source.
Solution Approach 2:
The system introduces an intermediary permission management layer between the training corpus and the model training process. This intermediary component automatically segments data based on permission requirements and manages access control, simplifying the complexity of coordinating multiple data sources with different access requirements.
3Object-affected harmful factors
If segmented training corpus is used instead of the entire corpus, then data security is improved, but the training time and processing resources increase
Solution Approach 1:
The training corpus is pre-segmented into permission-based subsets before the model training process begins. Permission requirements are predetermined and assigned to each segment in advance. This preliminary segmentation avoids the need for real-time data filtering during training, reducing processing overhead and training time.
4Object-affected harmful factors
If permission-based model selection is implemented, then unauthorized access prevention is improved, but the system complexity and model management overhead increase
Solution Approach 1:
The system dynamically selects appropriate models based on user permission levels at runtime. Instead of static model assignments, the system adapts model selection to the current user's permissions, allowing flexible access control without hardcoding permissions into each model deployment.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
In some examples, systems and methods for access control in relation to training machine learning models (e.g., large language models) are disclosed. More particularly, some embodiments of the present disclosure relate to training and management of language models. For example, a method includes: receiving a model permission requirement; receiving a training corpus; segmenting the training corpus into a segmented training corpus based at least in part on the model permission requirement; training a machine learning model using the segmented training corpus; and associating the trained machine learning model with the model permission requirement.