Evaluating Synthetic Data Quality: Advanced Techniques
Chapter 1: Foundations of Synthetic Data Evaluation
Defining Data Quality Dimensions
Challenges in Evaluating Generated Data
The Fidelity-Utility-Privacy Trade-off
Taxonomy of Evaluation Metrics
Setting Up an Evaluation Environment
Chapter 2: Advanced Statistical Fidelity Assessment
Multivariate Distribution Comparisons
Hypothesis Testing for Distributional Similarity
Correlation and Covariance Structure Analysis
Information-Theoretic Measures
Propensity Score Evaluation
Hands-on practical: Implementing Multivariate Tests
Chapter 3: Evaluating Machine Learning Utility
Train-Synthetic-Test-Real (TSTR) Methodology
Train-Real-Test-Synthetic (TRTS) Methodology
Comparing Downstream Model Performance Metrics
Assessing Feature Importance Consistency
Hyperparameter Optimization Effects
Hands-on practical: Running TSTR Evaluations
Chapter 4: Privacy Assessment Techniques
Understanding Privacy Risks in Synthetic Data
Membership Inference Attacks (MIAs)
Attribute Inference Attacks
Distance-Based Privacy Metrics
Differential Privacy Considerations (if applicable)
Hands-on practical: Implementing a Basic MIA
Chapter 5: Specialized and Model-Specific Metrics
Evaluating Synthetic Images: FID, IS, Precision, Recall
Evaluating Synthetic Text: Perplexity, BLEU Scores
Evaluating Synthetic Time-Series Data
Metrics for GAN Evaluation
Metrics for VAE Evaluation
Hands-on practical: Calculating FID for Image Data
Chapter 6: Building Comprehensive Evaluation Reports
Selecting Appropriate Metrics for the Task
Automating Evaluation Pipelines
Visualizing Evaluation Results Effectively
Interpreting and Communicating Findings
Benchmarking Different Synthetic Datasets
Practice: Generating a Quality Report Snippet
Understanding Privacy Risks in Synthetic Data
Was this section helpful?
- The Algorithmic Foundations of Differential Privacy, Cynthia Dwork and Aaron Roth, 2014 (Now Publishers) DOI: 10.1561/0400000042 - This book provides a foundational treatment of differential privacy, a mathematical framework used to quantify and limit privacy risks in data analysis and synthetic data generation.
- Membership Inference Attacks Against Machine Learning Models, Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov, 2017 2017 IEEE Symposium on Security and Privacy (SP) (IEEE) DOI: 10.1109/SP.2017.37 - This seminal paper introduced the concept and methodology of membership inference attacks, a significant privacy risk for models trained on sensitive data, including generative models.
- PATE-GAN: Generating Private Data with PATE, Jihye Yoon, Jinsung Yoon, Mihaela van der Schaar, 2019 International Conference on Learning Representations (ICLR) (ICLR) DOI: 10.48550/arXiv.1811.02562 - This paper proposes a method for generating differentially private synthetic data using Generative Adversarial Networks (GANs) and the PATE framework, directly addressing privacy in generative model design.