Machine Learning Classifier Influence Removal Verification
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Solution Overview
Problem
Conventional machine learning techniques are vulnerable to attacks from malicious adversaries seeking to access sensitive training data, particularly in adversarial environments, which poses challenges in complying with regulations like the GDPR's 'right to be forgotten' and protecting user data from model inversion attacks.
Innovation Solution
A method is implemented to verify and remove the influence of a user data point from a machine learning classifier using an influence function, updating the classifier's parameters without retraining, and calculating expected differences in loss to ensure compliance with data deletion requests, thereby protecting user data from exposure during model inversion attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional machine learning techniques are used to train classifiers, then classification performance is improved, but vulnerability to model inversion attacks increases
Solution Approach 1:
The patent applies preliminary action by calculating influence functions during the training phase to identify and remove user data points before the classifier is deployed. This proactive approach ensures that sensitive training data cannot be extracted through model inversion attacks at runtime, while still maintaining classification performance through careful influence removal that preserves important patterns.
Solution Approach 2:
The patent extracts and removes the harmful influence of specific user data points from the trained classifier using influence function calculations. By identifying and eliminating the contribution of individual training examples to the model parameters, the system removes the vulnerability to model inversion attacks while retaining the beneficial patterns from the remaining training data.
2Reliability
If user data is removed from training set to comply with GDPR, then data protection is improved, but retraining cost and time increase
Solution Approach 1:
The patent changes the parameters of the trained classifier by applying influence function-based updates to remove user data influence. Instead of retraining from scratch with modified data, the system adjusts the model parameters directly using the influence function formula, which provides a mathematical approach to decrementally update the classifier while complying with GDPR requirements.
Solution Approach 2:
The patent creates a modified version of the classifier parameters by applying influence function corrections to the original trained parameters. This copying approach allows the system to preserve the original trained model while generating a new parameter set that excludes specific user data influence, avoiding the need to retrain entirely.
3Productivity
If influence function is used to remove user data influence, then data removal efficiency is improved, but verification complexity increases
Solution Approach 1:
The patent implements feedback by calculating the difference between the original classifier parameters and the updated parameters after influence removal. This verification mechanism provides feedback on whether the user data influence has been successfully removed, allowing the system to confirm GDPR compliance through measurable parameter changes without requiring complex external verification processes.
Data Source
AI summary
Verifying that influence of a user data point has been removed from a machine learning classifier. In some embodiments, a method may include training a machine learning classifier using a training set of data points that includes a user data point, calculating a first loss of the machine learning classifier, updating the machine learning classifier by updating parameters of the machine learning classifier to remove influence of the user data point, calculating a second loss of the machine learning classifier, calculating an expected difference in loss of the machine learning classifier, and verifying that the influence of the user data point has been removed from the machine learning classifier by determining that the difference between the first loss and the second loss is within a threshold of the expected difference in loss.