Clearance Traversal System for Machine Learning Models
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Solution Overview
Problem
Financial institutions face challenges in utilizing user data due to clearance restrictions and model capacity limitations, leading to incomplete or faulty outcomes in machine learning models, such as incorrect credit card recommendations without considering mortgage obligations.
Innovation Solution
Implementing a system with multiple models having distinct clearances to traverse clearance-related issues, where a second model with superior clearance accesses secure data to generate a traversal outcome that corrects the faulty output of a first model, while preventing access to user data analyzed by the first model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a first machine learning model is used to provide product recommendations, then the model can generate outcomes based on available data, but the model produces faulty outcomes when clearance restrictions prevent access to relevant user data
Solution Approach 1:
A second machine learning model acts as an intermediary to bridge clearance restrictions. The second model receives the first model's faulty output, accesses additional secure data that the first model cannot access, and generates a traversal outcome that corrects the faulty recommendation. This intermediary model enables data flow across clearance boundaries without direct access by the first model.
Solution Approach 2:
The system segments the machine learning functionality into multiple models with different clearance levels. The first model handles data it has clearance for, while the second model handles data with higher clearance requirements. This segmentation allows each model to operate within its authorized scope while collectively producing accurate outcomes.
2Reliability
If multiple models with distinct clearances are implemented to traverse clearance restrictions, then accurate outcomes can be generated, but the system complexity increases
Solution Approach 1:
Multiple machine learning models with different clearance levels are merged into a unified system architecture. The models work together through defined interfaces where the second model's traversal outcome feeds back to correct the first model's output. This merging enables comprehensive data utilization while maintaining manageable system complexity through structured integration.
3Loss of information
If a model accesses user data beyond its clearance level, then complete information can be utilized, but data privacy and security are compromised
Solution Approach 1:
The second model serves as a secure intermediary that temporarily accesses data beyond the first model's clearance level. It processes this sensitive data to generate a traversal outcome without the first model directly accessing the restricted information. This intermediary approach enables complete data utilization while maintaining security boundaries and preventing unauthorized access.
Solution Approach 2:
Instead of allowing direct access to restricted user data, the system creates a processed copy or representation of the data through the second model's traversal outcome. This copy contains the necessary information to correct the first model's output without exposing the underlying sensitive data, thus maintaining privacy while achieving data completeness.
Data Source
AI summary
Apparatuses, methods, and computer program products are provided for data privacy and clearance traversal. An example method includes receiving a first model having an associated first clearance. The method further includes determining a faulty outcome of the first model due to the first clearance and receiving a second model having an associated second clearance. The method also includes inputting the faulty outcome of the first model to the second model and generating a traversal outcome of the second model. The method further include inputting the traversal outcome to the first model so as to traverse a clearance issue associated with the first clearance. The method also includes preventing access of the second model to user data analyzed by the first model in generating the faulty outcome.