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
Distance-Based Privacy Metrics
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- Measuring the Privacy of Synthetic Data, Jyoti D. Shringarpure and C. P. B. M. de Rooij, 2021 Transactions on Data Privacy, Vol. 14 (De Gruyter) DOI: 10.2478/tdp-2021-0004 - This paper provides a foundational discussion and formalization of distance-based privacy metrics like Distance to Closest Record (DCR) for synthetic data, offering detailed methodology and interpretation.
- An Evaluation of Data Synthesis Methods for Privacy Protection, Simon Beaulieu, Andrew C. T. C. Yip, Andrew G. L. Wong, 2021 (Statistics Canada) - This technical report evaluates various data synthesis methods and extensively uses distance-based metrics, including DCR and NNDR, to assess their privacy implications in practical settings.
- A survey on data synthesis: From techniques to applications, Bingbing Liu, Zhaokun Wang, Zhen Huang, Jianliang Xu, and Yunjun Gao, 2022 ACM Computing Surveys, Vol. 55 DOI: 10.1145/3547285 - This comprehensive survey provides a broad overview of data synthesis techniques and dedicated sections on privacy evaluation, contextualizing distance-based metrics within a wider range of privacy assessment approaches.