Layer-Subset Model Unlearning for Privacy-Compliant AI Output
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Solution Overview
Problem
Existing machine learning models trained on personal data face challenges in complying with data privacy regulations, such as GDPR and CCPA, as they are considered personal information and must be unlearned upon data removal requests, requiring inefficient retraining of the entire model to forget the data.
Innovation Solution
A machine learning model architecture is divided into two sets of layers, with a gate classifier distinguishing between data to keep and data to unlearn, and a low-rank adaptation module adjusting the second set of layers to output only non-personal data, optimizing training by reducing the number of parameters to be retrained.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire machine learning model is retrained to remove personal data, then data privacy compliance is improved, but computational cost and training time increase significantly
Solution Approach 1:
The machine learning model is divided into two distinct sets of layers: a first set of layers that processes input data and a second set of layers that generates output. This segmentation allows the system to selectively apply unlearning operations only to the first set of layers while preserving the second set, thereby reducing computational cost while maintaining privacy compliance.
Solution Approach 2:
The patent extracts and isolates the first set of layers that contain personal data representations for targeted unlearning. By separating these layers from the second set that generates clean output, the system can remove personal data influences without retraining the entire model, thus reducing computational burden while ensuring data privacy compliance.
2Reliability
If the entire machine learning model is retrained to remove personal data, then data privacy compliance is improved, but training time increases significantly
Solution Approach 1:
The machine learning model is divided into two distinct sets of layers: a first set of layers that processes input data and a second set of layers that generates output. This segmentation allows the system to selectively apply unlearning operations only to the first set of layers while preserving the second set, thereby reducing training time while maintaining privacy compliance.
Solution Approach 2:
The patent extracts and isolates the first set of layers that contain personal data representations for targeted unlearning. By separating these layers from the second set that generates clean output, the system can remove personal data influences without retraining the entire model, thus reducing training time while ensuring data privacy compliance.
3Measurement precision
If a gate classifier is introduced to distinguish data to keep from data to unlearn, then unlearning precision is improved, but device complexity increases
Solution Approach 1:
A gate classifier is introduced as an intermediary component between the input data and the first set of layers. This gate classifier processes input data and generates control signals that determine which data should be processed by the first set of layers for unlearning. While this adds some architectural complexity, it enables precise control over unlearning operations, improving unlearning precision by ensuring only relevant data is targeted.
Data Source
AI summary
Provided are a computer program product, system, and method for a machine learning model trained to not output data to unlearn. A query is processed at a first set of layers of the L layers of the machine learning model to determine interim output. The interim output is classified as data to unlearn or data to keep. The interim output is processed at a second set of layers of the L layers of the machine learning model to produce a first output in response to classifying the interim output as data to keep. The interim output is inputted to an edited machine learning model formed from the second set of layers to output second output in response to classifying the interim output as the data to unlearn from the machine learning model. The first output or the second output is returned as a response to the query.