Privacy-Preserving Tabular LLM Fine-Tuning Using DP-SGD
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Solution Overview
Problem
Existing machine learning models face challenges in privacy compliance during training and inference, leading to performance degradation due to privacy-preserving measures, making them less viable in applications requiring high performance.
Innovation Solution
A privacy-preserving fine-tuning process for large language models (LLMs) on tabular data using a pretrained LLM, converting tabular data into natural language strings, and incorporating differential privacy stochastic gradient descent (DP-SGD) to minimize parameter adjustments and protect sensitive information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If privacy-preserving measures are applied during machine learning model training and inference, then privacy compliance is improved, but model performance deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-processing tabular data into natural language format before model training. This transformation is performed in advance, allowing the model to work with privacy-preserving natural language representations while maintaining its ability to learn effective patterns, thus resolving the contradiction between privacy compliance and model performance
Solution Approach 2:
The patent changes the parameter representation by converting structured tabular data parameters into natural language text parameters. This parameter transformation allows the model to process data in a format that inherently preserves privacy while maintaining the information needed for high-performance predictions, thereby resolving the performance degradation issue
2Reliability
If tabular data is converted to natural language strings for LLM processing, then privacy protection is improved, but data processing complexity increases
Solution Approach 1:
The patent introduces natural language as an intermediary between structured tabular data and the LLM processing pipeline. This intermediary representation serves as a bridge that protects privacy by decoupling the original structured data from the model processing, while the conversion process manages complexity through systematic template-based transformation
3Reliability
If differential privacy SGD is used for fine-tuning, then privacy preservation is improved, but training overhead increases
Solution Approach 1:
The patent extracts and applies differential privacy techniques specifically to the gradient computation step in SGD fine-tuning. By isolating the privacy-preserving mechanism to the gradient update phase rather than applying it throughout the entire training pipeline, the solution achieves privacy preservation while minimizing the training overhead associated with privacy mechanisms
Data Source
AI summary
This specification relates to privacy-preserving model training on tabular data. In some aspects, a method includes receiving, by one or more computing devices, tabular data; serializing the tabular data into a natural language string in a natural language format; combining the natural language string and a prompt as an input to a pretrained large language model (LLM) to generate a predicted result, wherein a set of learned vectors are added into the pretrained LLM for fine-tuning the pre-trained LLM; fine-tuning the pretrained LLM using a differential privacy stochastic gradient descent (SGD) process, wherein fine-tuning the pretrained LLM comprises: determining values of the learned vectors that minimize a difference between the predicted result and the ground truth; receiving a request including test tabular data for a predication task; and generating, in response to the request for the prediction task, a prediction result for the test tabular data using the fine-tuned LLM.