Trainable Differential Privacy Using Membership Inference Loss
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Solution Overview
Problem
Existing differential privacy mechanisms for machine learning models are time-consuming and labor-intensive to tune for optimal privacy and utility, and they lack effective protection against both black-box and white-box membership inference attacks.
Innovation Solution
Incorporating a trainable differential privacy mechanism into the machine learning training process, using a confidence parameter that is iteratively tuned via backpropagation, to balance classification accuracy and privacy by combining class prediction and membership inference losses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual tuning of differential privacy parameters is used, then privacy protection level can be adjusted, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The system automatically determines optimal differential privacy parameters through self-service mechanisms including loss function analysis and iterative tuning, eliminating the need for manual parameter adjustment while maintaining appropriate privacy protection levels
Solution Approach 2:
The system implements feedback loops where the loss function (combining classification loss and membership inference loss) provides continuous information about privacy-utility tradeoffs, enabling automatic parameter adjustment based on model performance and privacy protection requirements
2Reliability
If strong differential privacy protection is applied, then membership inference attacks are prevented, but the utility and accuracy of the machine learning model deteriorates
Solution Approach 1:
The system dynamically adjusts differential privacy parameters (such as noise scale and privacy budget) based on the specific characteristics of the data, model architecture, and security requirements, optimizing the balance between privacy protection and model utility rather than applying fixed strong protection
Solution Approach 2:
The system applies differential privacy protection selectively and partially, focusing on protecting against membership inference attacks while maintaining model accuracy through targeted parameter selection that provides sufficient but not excessive privacy protection
3Reliability
If differential privacy mechanism is integrated into training process, then both black-box and white-box membership inference attacks are protected against, but the training complexity increases
Solution Approach 1:
The system merges the differential privacy mechanism with the machine learning training process by integrating loss function computation and parameter optimization into the standard training loop, allowing simultaneous optimization of model accuracy and privacy protection without separate complex procedures
Solution Approach 2:
The trained model serves multiple functions: it maintains high classification accuracy for its primary task while simultaneously providing robust protection against both black-box and white-box membership inference attacks, eliminating the need for separate security mechanisms
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly reduces the time and effort required for tuning, achieving optimal utility and privacy characteristics in machine learning models while protecting against both black-box and white-box membership inference attacks.
Implementation Method 1
transforming the training data comprises combining a confidence parameter with a sample from a Laplace distribution
Data Source
AI summary
Technologies are provided for training machine learning models using a differential privacy mechanism. Training data can be transformed using a differential privacy mechanism that comprises a trainable confidence parameter. The transformed training data can be used to generate class predictions using the machine learning model. A class prediction loss can be determined based on differences between the class predictions and actual classes for the training data. A membership inference loss can also be determined based on predictions that example records in the transformed data set are actual members of the original training data. The membership inference loss and the class prediction loss can be combined to generate a classifier loss that can be used to update the machine learning model and to update the trainable confidence parameter of the differential privacy mechanism. The training can be repeated multiple times until the combined classifier loss falls below a specified threshold.