Differential Privacy Model Training via Weight Loss Term
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Solution Overview
Problem
Differential privacy methods used in machine learning to protect training data often result in a significant reduction in model performance, making them unsuitable for certain applications.
Innovation Solution
Incorporating a joint loss function with both a predictive loss term and a weight loss term during the training of classification models with differential privacy, which minimizes the distance between the weights of the differential privacy-trained model and an unprotected model trained directly on the data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If differential privacy methods are used to protect training data, then privacy guarantees are improved, but model performance deteriorates
Solution Approach 1:
The loss function is segmented into two distinct components: a predictive loss term that ensures model accuracy and a weight loss term that enforces privacy constraints. This segmentation allows independent optimization of both privacy and performance objectives without one compromising the other.
Solution Approach 2:
The patent merges the predictive loss function and weight loss function into a joint loss function that simultaneously optimizes both model performance and privacy protection. The combined loss function L(θ) = L_predictive(θ) + L_weight(θ) integrates both objectives into a unified training framework.
2Measurement precision
If a joint loss function with weight loss term is used, then model utility is improved, but training complexity increases
Solution Approach 1:
The weight loss term incorporates a pre-trained model's weights as a reference, allowing the differential privacy model to learn from established weight patterns. This preliminary action provides a strong initialization that accelerates convergence and reduces training complexity despite the added loss term.
Solution Approach 2:
The weight loss term provides continuous feedback during training by comparing current weights against the reference model's weights. This feedback mechanism guides the optimization process, ensuring the model maintains both privacy guarantees and high utility without requiring complex external interventions.
Data Source
AI summary
Systems and methods include acquisition of training data comprising a plurality of target variable categories, training of a first classification model based on the training data, determination of node weights of the trained first classification model, and training of a second classification model using differential privacy and a first loss function including a weight loss term comparing the determined node weights of the trained first classification model to node weights of the second classification model.