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
Defining Data Quality Dimensions
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- Generating High-Fidelity Synthetic Data for Privacy-Preserving Applications, L. Xu, M. Skoularidou, X. Sun, M. van der Schaar, 2019 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.1902.04948 - This paper proposes a GAN-based method for generating synthetic data, focusing on balancing data utility and privacy protection. It illustrates the practical challenges in achieving high fidelity and strong privacy guarantees simultaneously, reflecting the FUP trade-off.
- Benchmarking Synthetic Data Generation Models, Y. Lu, S. Ding, R. Huang, F. Wu, 2022 ACM Transactions on Knowledge Discovery from Data, Vol. 17 DOI: 10.1145/3547271 - This paper provides a systematic benchmark of various synthetic data generation models. It details diverse evaluation metrics for statistical resemblance and machine learning performance, providing a framework for comparing different generation techniques.
- The Algorithmic Foundations of Differential Privacy, Cynthia Dwork, Aaron Roth, 2014 (Now Publishers) - This book serves as a principal resource for understanding differential privacy, a method for formally quantifying and preserving privacy in datasets. It lays the theoretical groundwork for privacy protection, which is essential for secure synthetic data generation.