Embedding-Based Resource Assignment for Private Data Recommendations
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Solution Overview
Problem
Existing high-performance computing (HPC) systems lack the ability to process and make recommendations based on user data that is secure and inaccessible, leading to inefficiencies and potential breaches of user privacy.
Innovation Solution
A data evaluation system that generates embeddings of both secure and accessible user data using an embedding scheme, allowing a machine learning model to process and recommend resources without decoding the secure data, thereby maintaining privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If HPC systems store confidential information in inaccessible secure manner, then data security is improved, but system ability to process and make recommendations based on user data deteriorates
Solution Approach 1:
The patent introduces embeddings as an intermediary representation layer between secure user data and system processing capabilities. User data is converted into embedding vectors that capture essential features while maintaining security, allowing the system to process and analyze data patterns without accessing the original confidential information. This mediator enables recommendation generation while preserving data accessibility restrictions.
Solution Approach 2:
The system creates a copy of user data in embedding space that preserves the essential characteristics and relationships of the original data without replicating the actual confidential information. These embedding copies can be processed, compared, and used for recommendations while the original secure data remains inaccessible, effectively copying functionality without copying sensitivity.
2Reliability
If HPC systems require user permission to access data, then user privacy control is improved, but system efficiency and ease of operation deteriorates
Solution Approach 1:
Embeddings serve as an intermediary that allows the system to operate on user data without requiring explicit user permission for each operation. Once data is embedded, the system can efficiently process, compare, and generate recommendations based on embedding vectors without needing to check user permissions or access controls for each processing step, significantly improving operational efficiency while maintaining privacy through the embedding layer.
3Productivity
If HPC systems convert inaccessible data to accessible form, then data processing capability is improved, but security and user trust deteriorates
Solution Approach 1:
The embedding representation acts as an intermediary that enables data processing capability without converting secure data to accessible form. The system processes embedding vectors rather than decrypting or decoding the original data, maintaining security and user trust while achieving the necessary processing capabilities for analysis and recommendation generation.
Data Source
AI summary
Systems and methods for generating user recommendations based on restricted and accessible parameter sets are described herein. The system may transmit a request for a first parameter set. The system may cause a first plurality of embeddings to be generated using an embedding model based on the first parameter set. The system may retrieve a second parameter set. The system may provide the second parameter set to a duplicate embedding model for generating a second plurality of embeddings. The system may generate one or more parameter sets. The system may generate one or more indications corresponding to one or more resources associated with the one or more parameter sets.