Machine Learning Model Unlearning Without Accuracy Loss
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Solution Overview
Problem
Existing methods for making machine learning models independent of specific training instances, such as differential privacy through noise addition, are insufficient in ensuring privacy and accuracy, especially when dealing with multiple records, and may not fully comply with privacy regulations like GDPR.
Innovation Solution
The system processes a model trained based on a loss function by removing undesired training instances from the dataset and adapting the model's parameters to optimize the objective function with respect to the remaining dataset, effectively making the model independent from those instances upon receiving a removal request, thereby maintaining accuracy and compliance with privacy regulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If noise is added to model outputs to make them independent from training records, then privacy protection is improved, but model output accuracy deteriorates
Solution Approach 1:
The patent extracts and removes specific training records from the training dataset that are identified for removal (e.g., due to privacy concerns or user requests). By taking out these specific records and retraining the model without them, the invention achieves privacy protection without adding noise to model outputs, thus maintaining accuracy while protecting individual data subjects.
Solution Approach 2:
Instead of adding noise to model outputs to achieve privacy (the conventional approach), the patent inverts the approach by removing specific training records from the dataset and retraining the model. This inversion allows privacy protection to be achieved at the data level rather than at the output level, avoiding the accuracy degradation that comes with noise addition.
2Reliability
If more noise is added to make model outputs independent from multiple training records, then privacy protection is improved, but model output accuracy deteriorates further
Solution Approach 1:
The patent identifies and extracts multiple specific training records that need to be removed for privacy protection. By removing these records from the training dataset and retraining the model without them, the invention achieves independence from multiple training records without adding noise, thereby maintaining model output accuracy while providing comprehensive privacy protection.
3Reliability
If the model is made independent from training instances through noise addition, then compliance with privacy regulations is improved, but the existing model still represents personal information allowing model inversion
Solution Approach 1:
The patent extracts and removes specific training records from the training dataset that contain personal information. By retraining the model without these records, the invention ensures that the model no longer represents or encodes information about removed data subjects, thereby preventing model inversion attacks while achieving compliance with privacy regulations like GDPR's right to be forgotten.
Solution Approach 2:
The patent changes the training dataset parameters by removing specific records identified for privacy protection. This parameter change in the training data leads to a new model configuration that no longer encodes information about removed individuals, achieving both regulatory compliance and elimination of model inversion risks.
Data Source
AI summary
A system for processing a model. The model provides a model output given an input instance. The model has been trained on a training dataset by iteratively optimizing an objective function including losses according to a loss function for training instances of the training dataset. Upon receiving a removal request message identifying one or more undesired training instances of the training dataset, the model is made independent from the one or more undesired training instances. To this end, the one or more undesired training instances are removed from the training dataset to obtain a remainder dataset, and an adapted model is determined for the remainder dataset. The parameters of the adapted model are first initialized based on the set of parameters of the trained model, and then iteratively adapted by optimizing the objective function with respect to the remainder dataset.