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
The Fidelity-Utility-Privacy Trade-off
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- A General Framework for Evaluating Synthetic Data, Samuel Lones, Michael E. J. Woolhouse, Alex J. Ball, Adam P. D. Smith, Lewis D. Griffin, 2023 Scientific Reports, Vol. 13 (Nature Portfolio) DOI: 10.1038/s41598-023-45547-1 - This paper introduces a framework for assessing synthetic data, directly addressing its multi-faceted quality, including fidelity, utility, and privacy. It provides a structured approach to evaluation.
- Private Synthetic Data Generation: A Survey, Hongyu Zhang, Xiaojuan Ma, Zhengyuan Wu, Xiang Zhang, 2022 arXiv preprint arXiv:2203.04787 - This survey reviews various differentially private synthetic data generation methods, highlighting the trade-offs between privacy, fidelity, and utility inherent in these techniques.
- The Algorithmic Foundations of Differential Privacy, Cynthia Dwork and Aaron Roth, 2014 (Now Publishers Inc.) - This foundational book provides a comprehensive explanation of differential privacy, its principles, mechanisms, and the inherent trade-offs with data utility, which is crucial for understanding the privacy dimension.