Layer-wise Distillation for Neural Network Privacy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Neural network models memorize training data, making them vulnerable to inference attacks that can reconstruct sensitive information, particularly in the context of machine learning-as-a-service paradigms where proprietary data is used for training, leading to privacy breaches.
Innovation Solution
Implementing a layer-wise differentially private distillation process that obfuscates the original training data by introducing noise into the operational parameters of each layer of a pre-trained neural network model, ensuring differential privacy and protecting against model inference attacks without requiring retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural network models are trained on proprietary data to achieve high accuracy, then model performance is improved, but the models become vulnerable to inference attacks that can reconstruct sensitive training data
Solution Approach 1:
The patent extracts the operational parameters (weights and biases) from the trained neural network model and applies differential privacy noise to them. This separation allows the model to maintain its functional accuracy while the parameters are protected against inference attacks, as the noise prevents accurate reconstruction of training data from the parameters.
Solution Approach 2:
The patent introduces differential privacy noise as an intermediary between the original training data and the operational parameters. This noise acts as a mediator that preserves the model's predictive capability while obscuring the relationship between parameters and training data, thereby preventing inference attacks without sacrificing model accuracy.
2Object-affected harmful factors
If differential privacy noise is added to operational parameters to protect training data, then privacy is improved, but model accuracy may deteriorate
Solution Approach 1:
The patent applies differential privacy parameter changes by adding controlled noise to the operational parameters. The noise magnitude is carefully calibrated to satisfy privacy requirements while minimizing impact on model accuracy. This parameter transformation allows the model to maintain sufficient accuracy for practical use while providing privacy protection against inference attacks.
3Object-affected harmful factors
If the neural network model is sanitized through layer-wise distillation to prevent data leakage, then security is improved, but the complexity of the protection mechanism increases
Solution Approach 1:
The patent segments the neural network into individual layers and applies differential privacy protection to each layer's operational parameters independently. This segmentation allows the complex protection mechanism to be applied systematically and efficiently, as each layer can be processed separately using the same differential privacy technique, reducing the overall complexity compared to a monolithic approach.
Solution Approach 2:
The patent performs preliminary action by adding differential privacy noise to the operational parameters before the model is deployed or shared. This preliminary protection ensures that even if the model is accessed or attacked after deployment, the training data cannot be reconstructed. The noise is added once during model preparation, and the protected parameters are then used throughout the model's lifecycle without requiring continuous complex processing.
Data Source
AI summary
Neural network protection mechanisms are provided. The neural network protection engine receives a pre-trained neural network computer model and forward propagates a dataset through layers of the pre-trained neural network computer model to compute, for each layer of the pre-trained neural network computer model, inputs and outputs of the layer. For at least one layer of the pre-trained neural network computer model, a differentially private distillation operation is performed on the inputs and outputs of the at least one layer to generate modified operational parameters of the at least one layer. The modified operational parameters of the at least one layer obfuscate aspects of an original training dataset used to train the pre-trained neural network computer model, present in original operational parameters of the at least one layer. The neural network protection engine generates a privatized trained neural network model based on the modified operational parameters.