Introduction to Synthetic Data for Machine Learning
Chapter 1: Understanding Synthetic Data
What is Synthetic Data?
Why Generate Artificial Data?
Real Data vs. Synthetic Data
Common Terminology
Potential Benefits
General Limitations
Quiz for Chapter 1
Chapter 2: Basic Methods for Data Generation
The Idea of Data Generation Models
Generating Data from Statistical Distributions
Introduction to Rule-Based Systems
Generating Simple Numerical Data
Generating Simple Categorical Data
Hands-on Practical: Create Basic Synthetic Data
Quiz for Chapter 2
Chapter 3: Generating Synthetic Tabular Data
Understanding Tabular Data Structure
Row Sampling Techniques
Independent Column Value Generation
Preserving Basic Column Correlations
Introduction to Data Anonymization Concepts
Hands-on Practical: Generate a Synthetic Table
Quiz for Chapter 3
Chapter 4: Introduction to Synthetic Image Data
Why Synthetic Data for Images?
Basic Image Properties: Pixels and Color
Creating Images with Simple Shapes and Patterns
Applying Noise and Simple Augmentations
Introduction to Rendering Simple Scenes
Challenges in Realistic Image Generation
Hands-on Practical: Generate Simple Synthetic Images
Quiz for Chapter 4
Chapter 5: Evaluating Synthetic Data Quality
Importance of Evaluation
Visual Inspection Methods
Basic Statistical Comparisons
Comparing Data Distributions
Concept of Fidelity vs. Utility
Quiz for Chapter 5
Chapter 6: Tools and Libraries Overview
Role of Software in Data Generation
Libraries for Basic Data Manipulation (NumPy, Pandas)
Introduction to Faker Library
Libraries for Simple Image Manipulation (Pillow, Scikit-image)
Finding Generation Tools
Quiz for Chapter 6
Introduction to Data Anonymization Concepts
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- k-Anonymity: A model for protecting privacy, Latanya Sweeney, 2002 International Journal on Uncertainty, Fuzziness and Knowledge-based Systems, Vol. 10 DOI: 10.1142/S0218488502001658 - Introduces the foundational k-anonymity model, a common method for achieving data anonymization by ensuring each record is indistinguishable from at least k-1 other records, which is pertinent to generalization techniques.
- Privacy-Preserving Data Publishing: An Overview, Raymond Chi-Wing Wong, Ada Wai-Chee Fu, 2010 Synthesis Lectures on Data Management, Vol. 2 (Morgan & Claypool Publishers) DOI: 10.2200/S00237ED1V01Y201003DTM002 - Provides a comprehensive overview of various traditional privacy-preserving data publishing techniques, including masking, generalization, and perturbation, and discusses their utility-privacy trade-offs.
- A Survey of Privacy-Preserving Synthetic Data Generation, Zhaohang Cao, Zhe Li, Chuan Wang, Ke Lyu, Yuzhen Wang, and Bing Yuan, 2023 ACM Computing Surveys, Vol. 56 (Association for Computing Machinery (ACM)) DOI: 10.1145/3631481 - A recent and comprehensive review of methods for generating synthetic data while preserving privacy, directly relevant to understanding synthetic data as an anonymization strategy.
- The Algorithmic Foundations of Differential Privacy, Cynthia Dwork and Aaron Roth, 2014 Foundations and Trends® in Theoretical Computer Science, Vol. 9 (Now Publishers Inc.) DOI: 10.1561/0400000042 - A foundational text introducing differential privacy, a strong mathematical definition of privacy crucial for building robust privacy-preserving systems and evaluating privacy risks in synthetic data.