Target Model Weight Editing via Reference Output Divergence
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Solution Overview
Problem
Current systems face challenges in effectively unlearning or forgetting data samples from machine learning models, particularly in compliance with data privacy regulations like GDPR and CCPA, where personal data must be removed without compromising the model's accuracy on remaining data sets.
Innovation Solution
A method is introduced where a target model, trained on both private and non-private data sets, is adjusted using optimized gradients to produce an edited model that mimics the output distribution of a reference model trained on non-private data, ensuring the removal of specific data samples while maintaining accuracy on the remaining data set.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If unlearning algorithms are used to remove data samples from a machine learning model, then data privacy compliance is improved, but model accuracy on remaining data deteriorates
Solution Approach 1:
The patent introduces a reference model trained only on non-private data as an intermediary to guide the unlearning process. The reference model's output distribution serves as a target for the target model to match during unlearning, preventing direct degradation of accuracy while removing private data influence. This intermediary mechanism allows the system to achieve both privacy compliance and accuracy preservation simultaneously.
Solution Approach 2:
The patent modifies the weight parameters of the target model through controlled gradient updates. By adjusting the weights to minimize divergence from the reference model's output distribution, the system changes the model parameters in a way that removes private data influence while maintaining overall performance. The parameter changes are optimized to balance privacy requirements with accuracy preservation.
2Measurement precision
If a target model is trained on both private and non-private data sets, then model performance is improved, but data privacy compliance deteriorates
Solution Approach 1:
The patent extracts the influence of private data from the target model by comparing its output distribution against a reference model trained only on non-private data. Through gradient-based weight adjustments, the private data influence is removed while preserving the beneficial effects of training on diverse data. This extraction process enables the model to maintain high performance while achieving privacy compliance.
3Reliability
If gradients are applied to update weights in the target model to forget data samples, then data removal effectiveness is improved, but model accuracy on remaining data deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the target model's output distribution is continuously compared against the reference model's output distribution. The gradient updates are guided by this feedback signal (divergence measurement), ensuring that weight adjustments remove private data influence while maintaining accuracy on remaining data. The feedback loop prevents excessive or harmful weight changes that would degrade model performance.
Data Source
AI summary
Provided are a computer program product, system, and method for editing a target model to forget data samples. Forget data samples of data samples to forget are inputted into a reference model, trained on a non-private data set, to produce reference output. The forget data samples to forget are inputted to a target model, trained on a total data set comprising the non-private data set and a private data set, to produce target output. The private data set includes the forget data samples A loss function is calculated to measure a divergence of the reference output and the target output. A determination is made of gradients that minimize an error of the loss function. Optimized gradients are calculated from the determined gradients. The optimized gradients are applied to update weights in the target model to produce an edited target model.