Private Synthetic Data Generation via Score-Based Diffusion Models
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Solution Overview
Problem
Modern machine learning models face challenges in sourcing large datasets in privacy-sensitive domains due to overparameterized neural networks providing minimal privacy, allowing adversaries to recover training data, and raising copyright concerns through data indistinguishability.
Innovation Solution
The methods and apparatus perform private synthetic data generation using score-based diffusion models, ensuring sampled data is distinct from a given set through functional formulations of ordinary differential equations and latent space design methods, eliminating the need for re-training and modifying pre-trained models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If pre-trained diffusion models are used for synthetic data generation, then model development time is reduced, but privacy protection is insufficient as adversaries can recover training data
Solution Approach 1:
The patent segments the training process into two distinct phases: (1) training the diffusion model on private data to learn the data distribution, and (2) generating synthetic data using the pre-trained model. This segmentation allows the model to be trained once on sensitive data and then reused multiple times for synthetic data generation without re-exposing the original data, thus maintaining privacy while enabling efficient model development.
Solution Approach 2:
The patent applies preliminary action by pre-training the diffusion model on the private dataset before actual synthetic data generation. The model learns the underlying data distribution in advance, and subsequent synthetic data generation processes use this pre-learned knowledge without requiring access to the original private data, thereby providing privacy protection while maintaining model effectiveness.
2Ease of operation
If pre-trained models are used without modification, then ease of operation is improved, but the ability to ensure data distinctness deteriorates
Solution Approach 1:
The patent changes parameters during the synthetic data generation process, specifically by adding controlled noise perturbations and applying differential privacy mechanisms. These parameter changes ensure that generated synthetic data points are distinct from the original training data while maintaining the overall data distribution characteristics, thus achieving both ease of operation and data distinctness.
Solution Approach 2:
The patent introduces an intermediary mechanism in the form of a noise addition process and differential privacy layer between the pre-trained model and the final synthetic data output. This intermediary ensures that even though the model is used without modification, the generated data is transformed to be distinct from the original training data, maintaining both operational simplicity and data distinctness.
3Reliability
If synthetic data is generated to be statistically similar to original data, then data utility is improved, but privacy leakage risk increases
Solution Approach 1:
The patent applies partial action by generating synthetic data that captures the essential statistical properties and distribution characteristics of the original data without perfectly reproducing all details. This partial replication maintains data utility for machine learning tasks while reducing privacy leakage risk by omitting sensitive individual variations present in the original data.
Solution Approach 2:
The patent converts the potential harm of statistical similarity (which could enable privacy attacks) into a benefit by deliberately introducing controlled variations and noise. These variations prevent exact reconstruction of original data points while maintaining sufficient statistical similarity for useful synthetic data applications, thus transforming the privacy risk into a protective mechanism.
Data Source
AI summary
An example apparatus includes interface circuitry, machine-readable instructions, and at least one processor circuit to be programmed by the machine-readable instructions to access a first set of samples associated with a diffusion model, the first set of samples including a plurality of input data samples, generate a representation of the first set of samples, sample the representation of the first set of samples to generate a representation of a second set of samples, and generate the second set of samples from the representation of the second set of samples, the second set of samples including a plurality of output data samples, an output data sample corresponding to an input data sample and being different from the corresponding input data sample.