A Combinatorial Approach to Synthetic Data Generation for Machine Learning.

Area of Science:

• Machine Learning
• Data Privacy
• Synthetic Data Generation

Background:

• Machine learning datasets frequently contain sensitive personal health and financial information, posing privacy risks.
• Existing synthetic data generation methods often require numerous samples, impacting downstream task efficiency.
• Current techniques involve encoding data, random sampling in latent space, and decoding to generate synthetic data.

Purpose of the Study:

• To develop an efficient synthetic data generation technique that minimizes sample requirements.
• To enhance the privacy preservation capabilities of synthetic data generation methods.
• To improve the performance of machine learning models using synthetic data.

Main Methods:

• A combinatorial approach to sampling the latent space is proposed, focusing on t-way interactions among latent dimensions.
• This method is motivated by findings that model predictions are often driven by interactions between a limited number of features.
• The approach generates synthetic data samples by utilizing these identified feature interactions.

Main Results:

• The combinatorial sampling approach requires fewer synthetic samples compared to traditional random sampling to achieve similar model performance.
• When combined with differential privacy, this method shows a smaller performance degradation than random sampling.
• Empirical results demonstrate the effectiveness of leveraging feature interactions for efficient synthetic data generation.

Conclusions:

• The proposed combinatorial sampling method offers a more efficient alternative for generating high-quality synthetic data.
• This technique improves the trade-off between data utility and privacy preservation in machine learning.
• The findings suggest that targeted sampling based on feature interactions can significantly enhance synthetic data generation processes.