Projection-Based Stochastic Gradient Descent for Label Differential Privacy
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Solution Overview
Problem
In high-privacy regimes, such as digital content management, differential privacy techniques often result in a low signal-to-noise ratio for machine learning models, leading to decreased performance, especially when both features and labels are privatized unnecessarily.
Innovation Solution
The implementation of a projection-based stochastic gradient descent technique that maintains label differential privacy by denoising gradients through projections, thereby improving the performance of machine learning models in high-privacy regimes without privatizing non-sensitive features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If differential privacy is applied to both features and labels, then privacy protection is strengthened, but training cost increases and performance decreases due to unnecessary noise in non-sensitive features
Solution Approach 1:
The patent segments the differential privacy application into two distinct parts: label privatization and feature processing. Labels are privatized using randomized response to protect sensitive outcome information, while features are processed through projection-based denoising rather than full privatization. This segmentation allows privacy protection where needed (labels) while preserving utility where not needed (features), resolving the contradiction between privacy and performance.
Solution Approach 2:
The patent extracts and removes the unnecessary noise component from features while retaining the essential signal. By identifying that features do not require full privatization, the method extracts only the necessary privacy protection (applied to labels) and removes excessive noise from features through projection-based denoising, thereby improving model performance without compromising privacy.
2Reliability
If randomized response is used for label privatization, then label differential privacy is achieved, but signal-to-noise ratio becomes extremely low in higher-privacy regimes
Solution Approach 1:
The patent introduces projection-based denoising as an intermediary process between label privatization and model training. The denoiser acts as a mediator that processes the noisy privatized labels and reconstructs cleaner gradient signals by projecting them onto the feature space. This intermediary step recovers the signal-to-noise ratio while preserving the differential privacy guarantees, allowing both privacy and measurement precision to coexist.
Solution Approach 2:
The patent changes the parameter representation from raw privatized labels to denoised gradients through projection. By transforming the data representation and applying denoising operations, the method changes the noise characteristics while maintaining the privacy parameter ε guarantees, thereby improving signal-to-noise ratio without sacrificing privacy protection.
3Measurement precision
If stochastic gradient descent for differential privacy is applied, then signal-to-noise ratio improves compared to randomized response, but both features and labels are privatized resulting in higher training cost
Solution Approach 1:
The patent extracts and removes the unnecessary feature privatization step from the training process. By recognizing that features do not require differential privacy protection, the method eliminates the associated computational overhead and noise injection for features, reducing training cost while maintaining the signal-to-noise ratio improvements through label-only privatization combined with projection-based denoising.
Solution Approach 2:
The patent segments the privacy protection mechanism to apply differential privacy only to labels rather than both features and labels. This segmentation reduces the scope of privatization operations, thereby lowering training cost and computational overhead while maintaining adequate privacy protection for sensitive label information.
Data Source
AI summary
Aspects of the disclosure are directed to implementing a projection-based stochastic gradient descent technique that maintains label differential privacy when training one or more machine learning models. The technique includes denoising gradients by exploiting projections when training the machine learning models to improve performance of the trained machine learning models while maintaining label differential privacy. For instance, the projection-based stochastic gradient descent technique can improve performance of machine learning models in higher-privacy regimes, such as digital content management.