Synthetic Data Generation for Privacy-Preserving ML Training
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Solution Overview
Problem
Current machine-learning models are susceptible to malicious attacks due to the use of non-privacy compliant user data in training, which is time-consuming and costly to manage, and incorporating privacy mechanisms leads to larger and more complex models.
Innovation Solution
A system and method for generating synthetic privacy-preserving training data using a generative adversarial network (GAN) model with differential privacy, which processes true non-private training data to produce synthetic labeled data for language classification models, reducing the need for large datasets and human labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If privacy mechanisms are incorporated into ML models to ensure user data confidentiality, then user privacy is protected, but the model size and complexity increase
Solution Approach 1:
The system separates the privacy protection function into a distinct synthetic data generation model that operates independently from the target ML model. The synthetic data generation model processes raw user data to create privacy-preserving synthetic datasets, which are then used to train the target model. This segmentation allows privacy protection without modifying the target model's architecture or complexity.
Solution Approach 2:
Synthetic data acts as an intermediary between raw user data and the target ML model. The synthetic data generation model transforms sensitive user data into synthetic datasets that preserve statistical properties while removing personally identifiable information. This intermediary layer enables the target model to learn from privacy-preserving data without direct exposure to sensitive information, avoiding the need to embed privacy mechanisms within the target model itself.
2Reliability
If privacy mechanisms are incorporated into ML models to ensure compliance, then privacy compliance is achieved, but the models become larger and more complex
Solution Approach 1:
The privacy compliance function is segmented into a separate synthetic data generation component rather than being integrated into the target ML model. This allows the target model to remain compact while still achieving privacy compliance through the use of synthetic training data generated by the separate privacy-preserving model.
Solution Approach 2:
The system creates synthetic copies of user data that replicate the statistical properties and patterns of the original data while removing sensitive information. These synthetic copies are then used for training the target model, enabling privacy compliance without requiring the target model to process or store sensitive user data, thus avoiding increased model size.
3Measurement precision
If labeled training data with large datasets is used to train ML models, then model accuracy is improved, but the process becomes time-consuming and costly due to human labeling requirements
Solution Approach 1:
The synthetic data generation model performs self-service by automatically generating labeled training data without requiring human annotators. The model takes unlabeled user data as input and produces synthetic datasets with automatically assigned labels based on the learned patterns from training examples. This eliminates the time-consuming and costly human labeling process while still providing sufficient labeled data for accurate model training.
Solution Approach 2:
The system changes the parameter of data quantity by generating synthetic data that can be produced in large volumes automatically. Instead of being limited by the slow pace of human labeling, the synthetic data generation model can create extensive labeled datasets rapidly, providing sufficient training data for high model accuracy without the time constraints of manual labeling.
4Productivity
If non-privacy compliant user data is used for training, then training efficiency is maintained, but the models become susceptible to malicious attacks
Solution Approach 1:
Synthetic data serves as an intermediary that breaks the direct connection between sensitive user data and the target ML model. The synthetic data generation model processes user data to create privacy-preserving synthetic datasets, which are then used for training. This intermediary layer maintains training efficiency since the process is automated, while simultaneously improving model security by ensuring the training data is privacy-compliant and resistant to malicious attacks.
Data Source
AI summary
A method and system for generating synthetic privacy preserving training data for training a language classifier machine-learning (ML) model includes receiving a request to generate the synthetic privacy-preserving training data for the language classifier ML model, retrieving labeled training data associated with training the language classifier ML model, providing the labeled training data, one or more privacy parameters, and a domain type associated with the labeled training data to a synthetic data generation ML model, the synthetic data generation ML model being configured to generate synthetic training data in a privacy-persevering manner, receiving synthetic privacy-preserving training data as an output from the synthetic data generation ML model, and providing the synthetic privacy preserving training data to the language classifier ML model for training the language classifier ML model in classifying text.