Differentially Private Diffusion Models for Privacy-Preserving Synthetic Data
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Solution Overview
Problem
Existing content generation systems face challenges in balancing privacy and performance criteria, particularly in generating high-quality images while ensuring that sensitive information from training data is not reconstructed or inferred from the output.
Innovation Solution
The implementation of differentially private machine learning models, such as diffusion models, using techniques like differential privacy stochastic gradient descent (DP-SGD) to add noise to training data, allowing for better image quality while maintaining privacy by reducing overall stochasticity without depleting the privacy budget, and utilizing combinations of public and private data for training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional generative models are trained on training data, then the quality of generated content can be improved, but sensitive information regarding entities in the training data can be reconstructed or inferred from the output
Solution Approach 1:
The patent applies differential privacy by adding carefully calibrated noise to the training process, converting the harmful effect of privacy leakage into a beneficial privacy protection mechanism. The noise is added to gradient updates during training, ensuring that the model learns general patterns without memorizing sensitive individual data points, thus preventing reconstruction of private information while maintaining useful generation capabilities
Solution Approach 2:
The patent modifies the training parameters by incorporating privacy budget constraints and noise scaling factors into the optimization process. By changing how the model is trained (using DP-SGD with clipped gradients and controlled noise addition), the system achieves a balance between learning quality patterns and preventing overfitting to sensitive individual examples
2Object-affected harmful factors
If noise is added to training data to protect privacy, then privacy criteria can be met, but the quality of generated content deteriorates
Solution Approach 1:
The patent applies partial noise addition strategy where noise is added only to specific portions of the training process and at specific stages. Rather than adding excessive noise that would completely degrade quality, the system uses controlled, partial noise addition with carefully selected magnitude and timing to achieve sufficient privacy protection while preserving essential learning signals
Solution Approach 2:
The patent implements periodic privacy protection mechanisms where noise is added at specific intervals during training rather than continuously. The system alternates between phases of normal gradient descent and phases with privacy-preserving noise addition, allowing the model to periodically learn from clean gradients while maintaining privacy bounds through intermittent noise injection
3Object-affected harmful factors
If differential privacy noise is added during training, then privacy criteria can be satisfied, but the privacy training budget is depleted quickly
Solution Approach 1:
The patent applies preliminary privacy budget allocation strategies where the total privacy budget is pre-divided into phases or batches. By planning noise addition in advance and allocating privacy budget before training begins, the system avoids depleting the entire budget quickly and can sustain privacy-protected training over extended periods with multiple training iterations
Solution Approach 2:
The patent implements dynamic noise scaling where the amount of noise added adapts during training based on the current state of the model and remaining privacy budget. The noise magnitude is adjusted dynamically - larger noise early when privacy budget is abundant, smaller noise later as budget depletes - allowing the training to continue longer than static noise approaches
Data Source
AI summary
In various examples, systems and methods are disclosed relating to differentially private generative machine learning models. Systems and methods are disclosed for configuring generative models using privacy criteria, such as differential privacy criteria. The systems and methods can generate outputs representing content using machine learning models, such as diffusion models, that are determined in ways that satisfy differential privacy criteria. The machine learning models can be determined by diffusing the same training data to multiple noise levels.