Private Deep Learning via Teacher-Student Distillation
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Solution Overview
Problem
Existing privacy-preserving deep learning methods often lead to performance degradation when training neural networks with sensitive information, making it challenging to maintain privacy while ensuring model accuracy.
Innovation Solution
The implementation of a private deep teacher-student learning system that uses perturbed teacher model information to train student models, ensuring privacy by applying noise to sensitive data and utilizing multi-layer knowledge transfer through hint learning and distillation learning, while maintaining model performance using public data for student training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If privacy-preserving methods are applied to train neural networks with sensitive information, then privacy is protected, but model performance degrades
Solution Approach 1:
The training process is segmented into two distinct phases: a teacher training phase that uses sensitive data to generate training signals, and a student training phase that uses only public data. This segmentation allows privacy protection during student training while maintaining model accuracy through knowledge transfer from the teacher model trained on sensitive data.
Solution Approach 2:
The teacher model acts as an intermediary between the sensitive data and the student model. It processes sensitive data during training and outputs perturbed training signals that can be used by the student model without directly exposing the sensitive data, thus protecting privacy while preserving learning effectiveness.
2Manufacturing precision
If sensitive data is used for training, then model accuracy is improved, but privacy is compromised
Solution Approach 1:
The teacher model is trained preliminarily on sensitive data before the student model training begins. This preliminary action extracts and transfers knowledge from the sensitive data through perturbed signals, allowing the student model to achieve accurate training using only public data, thus preventing direct exposure of sensitive data.
Solution Approach 2:
Instead of directly using sensitive data for student training, the system creates a copy of the knowledge from the teacher model through perturbed training signals. The student model learns from this copied knowledge rather than from the original sensitive data, preserving privacy while maintaining learning effectiveness.
3Reliability
If perturbation is applied to teacher model information, then privacy is preserved, but information quality decreases
Solution Approach 1:
The system changes the parameter of the training signals by applying controlled perturbation to the teacher model outputs. This parameter change protects privacy by obscuring the direct connection to sensitive data while maintaining sufficient information quality for effective student model training through knowledge distillation.
Data Source
AI summary
A method for training parameters of a student model includes receiving one or more teacher models trained using sensitive data. Each teacher model includes one or more intermediate layers and a prediction layer coupled to the one or more intermediate layers. The method includes receiving, from the one or more teacher models, one or more intermediate layer outputs and one or more prediction layer outputs respectively based on public data. Student model training is performed to train parameters of the student model based on the intermediate layer outputs and prediction layer outputs of the one or more teacher models.