Synthetic Data Generator

Design synthetic data generation pipelines that produce privacy-preserving, statistically faithful datasets for ML training, testing, and data sharing using GANs, copulas, and differential privacy.

Gold

v1.0.00 activationsAI & Machine LearningTechnologyexpert

SupaScore

Research Quality (15%)

8.5

Prompt Engineering (25%)

8.5

Practical Utility (15%)

8.5

Completeness (10%)

8.5

User Satisfaction (20%)

Decision Usefulness (15%)

8.5

Best for

▸Creating GDPR-compliant synthetic datasets for cross-border ML model training
▸Generating test data for healthcare applications that preserves clinical patterns without HIPAA violations
▸Building realistic financial transaction datasets for fraud detection model development
▸Producing synthetic customer data for A/B testing without exposing real user information
▸Creating augmented training sets for rare disease classification models with differential privacy guarantees

What you'll get

●Synthetic tabular dataset with matching statistical distributions, correlation matrices, and privacy budget analysis showing epsilon values
●Technical report comparing original vs synthetic data quality metrics (KL divergence, correlation preservation, univariate distributions) with privacy risk scores
●Production-ready data generation pipeline code with configurable privacy parameters and automated quality validation checks

Not designed for ↓

×Generating creative content like images, text, or videos for marketing purposes
×Creating synthetic data without statistical validation or privacy analysis
×Replacing real data collection strategies or primary research methodologies
×Generating production-ready datasets without proper bias and fairness auditing

Expects

Original dataset with clear schema, privacy requirements (GDPR/HIPAA), intended use case, and quality metrics for statistical fidelity validation.

Returns

Privacy-preserving synthetic dataset with generation methodology report, statistical utility metrics, privacy risk assessment, and validation test results.

Evidence Policy

Enabled: this skill cites sources and distinguishes evidence from opinion.

synthetic-datadifferential-privacydata-generationctganprivacy-preserving-mldata-augmentationsdvtabular-datatest-datamachine-learningdata-privacy

Research Foundation: 8 sources (3 official docs, 1 paper, 1 books, 2 academic, 1 industry frameworks)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.

Version History

v1.0.02/16/2026

Initial release

Prerequisites

Use these skills first for best results.

Data Quality EngineerGold

Works well with

Data Privacy Compliance AdvisorGold Dataset Curation SpecialistGold Feature Engineering StrategistGold GDPR Technical Implementation EngineerGold ML Model Evaluation ExpertGold

Need more depth?

Specialist skills that go deeper in areas this skill touches.

AI Ethics & Bias AuditorGold Federated Learning ArchitectGold

Common Workflows

Privacy-Safe ML Pipeline

Generate privacy-preserving synthetic training data, validate model performance, and audit for bias before production deployment

synthetic-data-generator→ML Model Evaluation Expert→AI Ethics & Bias Auditor

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