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
Train-Synthetic-Test-Real (TSTR) Methodology
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- Synthetic datasets for statistical disclosure control: Theory and implementation, Jörg Drechsler, 2011 Vol. Lecture Notes in Statistics 201 (Springer-Verlag New York Inc.) DOI: 10.1007/978-1-4419-7419-1 - A book on synthetic data for privacy, including methods to assess data usefulness for analysis tasks.
- Evaluating the quality of synthetic data generated by deep learning models: a comprehensive survey, Damien Biau, Romain Tavenard, Rémi Flamary, 2023 Artificial Intelligence Review, Vol. 56 (Springer US) DOI: 10.1007/s10462-023-10515-w - A recent survey covering various aspects of synthetic data quality assessment, including utility metrics like TSTR.
- Measuring data utility for privacy-preserving synthetic data, Yang Zhao, Weijie Sun, Ji-Won Kim, Jiexin Yu, Liqiang Wang, 2022 Journal of Biomedical Informatics, Vol. 133 (Elsevier) DOI: 10.1016/j.jbi.2022.104164 - This article presents methods for assessing the analytical value of synthetic data, particularly in privacy contexts.
- Modeling Tabular Data using Conditional GANs, Lei Xu, Kalyan Veeramachaneni, 2019 Advances in Neural Information Processing Systems, Vol. 32 (Neural Information Processing Systems Foundation, Inc.) DOI: 10.55919/neurips-2019-1412 - Introduces CTGAN and demonstrates evaluation of synthetic data quality through machine learning tasks, consistent with TSTR.