Target Model Training Using Influence Regularization Against Data Leakage
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Solution Overview
Problem
Existing methods for defending against membership inference attacks in machine learning models are inefficient and require complex processes, such as preparing a binary classifier to maximize its gain, which complicates the training of target models.
Innovation Solution
A machine learning apparatus and method that calculates an influence function indicating sensitivity of input data on model parameters and uses this function as a regularization term during training to enhance robustness against membership inference attacks, eliminating the need for an attack discriminator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Adversarial Regularization is used to defend against membership inference attacks, then robustness against attacks is improved, but device complexity increases due to requiring a binary classifier and max-min game process
Solution Approach 1:
The patent extracts and eliminates the binary classifier component from the Adversarial Regularization framework. By removing the attack discriminator and its associated max-min game process, the solution simplifies the training architecture while maintaining defense capabilities through influence function-based regularization alone.
Solution Approach 2:
The target model performs self-defense against membership inference attacks through influence function calculation and regularization applied during its own training process. The model uses its training data and parameters to compute influence functions, which then directly regularize its learning without requiring external attack simulations or additional classifier components.
2Reliability
If Adversarial Regularization with binary classifier is used, then robustness against membership inference attacks is improved, but training time increases due to repeated max-min optimization
Solution Approach 1:
The patent removes the iterative max-min optimization loop and binary classifier training processes that consume significant training time. By eliminating these components, the solution reduces computational overhead while maintaining defense effectiveness through direct influence function regularization applied during standard training.
Solution Approach 2:
The influence function is calculated and applied as a regularization term during the target model's training process itself, rather than requiring separate pre-training or post-processing steps. This preliminary integration of defense mechanisms into the core training loop eliminates the need for repeated max-min games and reduces overall training time.
3Device complexity
If influence function is used as regularization term, then training process is simplified, but calculation complexity of influence function increases
Solution Approach 1:
The patent replaces complex iterative optimization mechanisms (max-min games, binary classifier training) with direct mathematical calculation of influence functions. By substituting the mechanical iterative process with a analytical influence function calculation approach, the solution simplifies the overall training process while managing computational complexity through efficient influence function computation.
Data Source
AI summary
A machine learning apparatus includes an influence function calculation part which calculates an influence function indicating sensitivity that input data has on parameters of a target model and a target model training part which trains the target model using the influence function as a regularization term.