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
Differential Privacy Considerations (if applicable)
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- The Algorithmic Foundations of Differential Privacy, Cynthia Dwork and Aaron Roth, 2014 (Foundations and Trends in Theoretical Computer Science) DOI: 10.1561/0400000042 - This definitive monograph on differential privacy covers its formal definition, mechanisms, composition theorems, and privacy guarantees, serving as a foundational text for understanding the subject.
- Deep Learning with Differential Privacy, Martín Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang, 2016 Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (Association for Computing Machinery (ACM)) DOI: 10.1145/2976749.2978318 - Introduces differentially private stochastic gradient descent (DP-SGD), a fundamental technique for training machine learning models with DP, highly relevant for synthetic data generation using deep learning.
- Verifying Differential Privacy of Complex Programs, Xi Li, Yuan Sun, Ninghui Li, 2021 30th USENIX Security Symposium (USENIX Security 21) (USENIX Association) DOI: 10.5555/3506169.3506381 - This paper addresses the practical challenges of verifying differential privacy guarantees in complex software programs, offering insights into algorithm analysis and implementation audit techniques.