Synthetic Data Generation With Differential Privacy for Model Training
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Solution Overview
Problem
Existing privacy-preserving data processing methods require significant domain knowledge and are often tailored to specific use cases, posing challenges in deploying models without violating privacy, and there is a risk of sensitive information leakage.
Innovation Solution
Utilizing generative adversarial networks (GANs) to synthesize privacy-preserving artificial data through differential privacy techniques, allowing models to be trained in a privacy-preserving manner without requiring domain-specific knowledge, and ensuring that the generated data maintains privacy even under arbitrary post-processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If privacy-preserving methods are used to process sensitive data, then privacy protection is improved, but the complexity of implementation increases due to requiring significant domain knowledge and tailoring to specific use cases
Solution Approach 1:
The patent creates synthetic copies of sensitive data that preserve statistical properties and relationships without containing actual private information. The generative model learns from real data and produces artificial datasets that can be used for analysis without exposing sensitive information, thereby protecting privacy while avoiding complex privacy-preserving implementation
Solution Approach 2:
The patent develops a universal generative framework that can be applied across multiple domains and use cases (financial data, health data, etc.) without requiring significant customization. The system provides a general-purpose solution that works for various types of sensitive data, reducing the need for domain-specific tailoring and expert knowledge
2Measurement precision
If real sensitive data is used for training models, then model accuracy is improved, but the risk of sensitive information leakage increases
Solution Approach 1:
The system generates synthetic data copies that replicate the statistical properties, distributions, and relationships of real sensitive data without containing actual private information. These synthetic copies enable model training with the same accuracy as real data would provide, while eliminating the risk of sensitive information leakage since the synthetic data contains no actual private information
Solution Approach 2:
The generative model acts as an intermediary between real sensitive data and the training process. Instead of directly using real sensitive data, the system uses the generative model to produce synthetic data that serves as a safe mediator, allowing model training to proceed with high accuracy while blocking the path for sensitive information leakage
3Reliability
If privacy regulations are enforced to protect user data, then individual privacy rights are improved, but the ability to use user data for useful services is reduced
Solution Approach 1:
The system creates synthetic data copies that preserve the utility and statistical properties needed for useful services (recommendation systems, fraud detection, etc.) while protecting privacy rights. Organizations can train models and perform analysis on synthetic data with the same productivity and service quality as if using real data, but with privacy regulations satisfied since no actual private information is exposed
Solution Approach 2:
The system transforms real sensitive data into synthetic data by changing key parameters - specifically, it modifies the data representation to remove identifying information while preserving statistical properties. This parameter transformation allows the data to maintain its utility for services while meeting privacy regulation requirements
Data Source
AI summary
Embodiments of the present disclosure are directed to the methods and systems for generating artificial data records from (potentially private or sensitive) data records in a privacy-preserving manner, particularly using machine learning models such as generative adversarial networks (GANs). Such artificial data records can be used in place of the real data in data analysis applications, such as training machine learning models. These artificial data records can be generated such that they do not (or have a low or negligible probability of) leaking information from the data records used to generate the artificial data records. As a result, artificial data records (or any machine learning models trained to generate such artificial data records) can potentially be published or distributed without violating rules, regulations, or laws restricting the transmission of sensitive data.