Synthetic Data Generation

Description

Synthetic data generation creates artificial datasets that aim to preserve the statistical properties, distributions, and relationships of real data whilst containing no actual records from real individuals. The technique encompasses various approaches including generative adversarial networks (GANs), variational autoencoders (VAEs), statistical sampling methods, and privacy-preserving techniques like differential privacy. Beyond privacy protection, synthetic data serves multiple purposes: augmenting limited datasets, balancing class distributions, testing model robustness, enabling data sharing across organisations, and supporting fairness assessments by generating representative samples for underrepresented groups.

Example Use Cases

Privacy

Creating realistic but synthetic electronic health records for developing and testing medical diagnosis algorithms without exposing real patient data, enabling secure collaboration between healthcare institutions.

Fairness

Generating synthetic samples for underrepresented demographic groups in a hiring dataset to train fair recruitment models, ensuring all groups have sufficient representation for bias testing and mitigation.

Reliability

Augmenting limited training data for rare medical conditions by generating synthetic patient records, improving model reliability and performance on edge cases where real data is insufficient.

Safety

Creating synthetic financial transaction data for testing fraud detection systems in development environments, avoiding exposure of real customer financial information whilst maintaining realistic attack patterns.

Limitations

May not capture all subtle patterns, correlations, and edge cases present in real data, potentially leading to reduced model performance when deployed on actual data with different characteristics.
Generating high-quality synthetic data that maintains both statistical fidelity and utility requires sophisticated techniques and substantial computational resources, especially for complex, high-dimensional datasets.
Privacy-preserving approaches may still risk information leakage through statistical inference attacks, membership inference, or model inversion, requiring careful privacy budget management and validation.
Synthetic data may inadvertently amplify existing biases in the original data or introduce new biases through the generation process, particularly in generative models trained on biased datasets.
Validation and quality assessment of synthetic data is challenging, as traditional metrics may not adequately capture whether the synthetic data preserves the relationships and patterns needed for specific downstream tasks.

Resources

sdv-dev/SDV

Software Package

An evaluation framework for synthetic data generation models

Research Paper•Ioannis E. Livieris et al.•Apr 13, 2024

Synthetic Data — SecureML 0.2.2 documentation

Documentation

How to Generate Real-World Synthetic Data with CTGAN | Towards ...

Tutorial

Related Techniques

Name	Description	Assurance Goals
Feature Attribution with Integrated Gradients in NLP	Applies Integrated Gradients to natural language processing models to attribute prediction importance to individual input tokens, words, or subword units. This technique computes gradients along a straight-line path from a baseline input (typically all-zeros, padding tokens, or neutral text) to the actual input, integrating these gradients to obtain attribution scores. Unlike vanilla gradient methods, Integrated Gradients satisfies axioms of sensitivity and implementation invariance, making it particularly valuable for understanding transformer-based language models where token interactions are complex.	Explainability Fairness Safety
SHapley Additive exPlanations	SHAP explains model predictions by quantifying how much each input feature contributes to the outcome. It assigns an importance score to every feature, indicating whether it pushes the prediction towards or away from the average. The method systematically evaluates how predictions change as features are included or excluded, drawing on game theory concepts to ensure a fair distribution of contributions.	Explainability Fairness Reliability
Permutation Importance	Permutation Importance quantifies a feature's contribution to a model's performance by randomly shuffling its values and measuring the resulting drop in predictive accuracy. If shuffling a feature significantly degrades the model's performance, that feature is considered important. This model-agnostic technique helps identify which inputs are genuinely driving predictions, rather than just being correlated with the outcome.	Explainability Reliability
Data Version Control	Data Version Control (DVC) is a Git-like version control system specifically designed for machine learning data, models, and experiments. It tracks changes to large data files, maintains reproducible ML pipelines, and creates a complete audit trail of data transformations, model training, and evaluation processes. DVC works alongside Git to provide end-to-end lineage tracking from raw data through preprocessing, training, and deployment, enabling teams to reproduce any model version and understand exactly how datasets evolved throughout the ML lifecycle.	Transparency Reliability
Attribute Removal (Fairness Through Unawareness)	Attribute Removal (Fairness Through Unawareness) ensures fairness by completely excluding protected attributes such as race, gender, or age from the model's input features. While this approach prevents direct discrimination, it may not eliminate bias if other features are correlated with protected attributes (proxy discrimination). This technique represents the most basic fairness intervention but often needs to be combined with other approaches to address indirect bias through seemingly neutral features.	Fairness Transparency
Counterfactual Fairness Assessment	Counterfactual Fairness Assessment evaluates whether a model's predictions would remain unchanged if an individual's protected attributes (race, gender, age) were different, whilst keeping all other causally legitimate factors constant. The technique requires constructing a causal graph that maps relationships between variables, then using do-calculus or structural causal models to simulate counterfactual scenarios. For example, it asks: 'Would this loan application still be approved if the applicant were a different race, holding constant their actual qualifications and economic circumstances?' This individual-level fairness criterion helps identify when decisions depend improperly on protected characteristics.	Fairness