Natural Language Teacher Artifacts for Privacy-Aware Model Updating
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Solution Overview
Problem
Existing methods for updating neural networks face challenges in privacy awareness, computational efficiency, and architecture agnosticism when sharing data among systems, leading to inefficiencies and potential breaches of privacy.
Innovation Solution
A collaborative data acquisition system generates non-private natural language teacher artifacts using teacher computer systems, allowing a student neural network to be updated in a privacy-aware and computationally efficient manner, regardless of underlying neural network architectures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If data is shared directly among systems to improve neural network performance, then the quality of machine learning outputs is improved, but privacy restrictions are violated and data security is compromised
Solution Approach 1:
The patent introduces a mediator system that receives private data from multiple systems, processes it through controlled transformations, and generates synthetic training data that preserves statistical properties without exposing sensitive information. This intermediary layer enables knowledge transfer while maintaining privacy boundaries.
Solution Approach 2:
Instead of sharing original private data, the system creates synthetic copies that replicate the statistical characteristics and patterns of the source data. These synthetic datasets serve as proxies for training neural networks, achieving similar performance improvements without copying actual sensitive information.
2Productivity
If raw private data is transferred among systems to update neural networks, then the performance on specific tasks is improved, but computational costs and data communication expenses increase significantly
Solution Approach 1:
The system extracts only the essential statistical properties and patterns from private data, separating these transferable insights from the sensitive raw information. By extracting and transmitting only the necessary statistical characteristics, the system reduces communication overhead and computational requirements while maintaining task performance improvements.
Solution Approach 2:
The patent transforms data from its original high-dimensional raw format into compressed statistical parameter representations. This parameter transformation reduces the volume of data that needs to be communicated and processed, lowering computational expenses while preserving the essential information needed for neural network improvement.
3Productivity
If data is shared among systems with different neural network architectures, then task-specific performance is improved, but the system becomes dependent on specific architecture formats
Solution Approach 1:
The system generates synthetic training data with universal statistical properties that can be applied across different neural network architectures. The synthesized datasets are designed to be architecture-agnostic, allowing the same data to improve performance on various model types without requiring architecture-specific transformations.
Solution Approach 2:
The patent separates the statistical properties of data from the specific neural network architecture requirements. By segmenting the data transformation process into architecture-independent statistical analysis and architecture-agnostic synthetic data generation, the system enables broad applicability across different model formats.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for updating a student neural network in a privacy aware (compliant with private data sharing restrictions) manner using additional data generated from other neural networks to improve the quality of machine learning task outputs. In particular, a system receives a request to generate additional data for a machine learning task, uses teacher computer systems to generate natural language teacher artifacts, updates a student neural network using the generated teacher artifacts, and processes inputs for the machine learning task to generate improved quality outputs for the machine learning task.