Models in VeriSynth

VeriSynth uses machine learning models to learn the statistical structure of real data and generate synthetic data that behaves like the real world — without exposing sensitive records. Each model is designed to balance realism, privacy, and computational efficiency.
The system is modular, so users can plug in new synthesis engines over time.

Current Model: Gaussian Copula (GC)

The Gaussian Copula Synthesizer is the first and default model in VeriSynth Core.
It’s a fast, deterministic, and lightweight model for tabular synthetic data — perfect for most structured datasets.

How It Works

Learn relationships
GC learns pairwise correlations between all columns in your dataset.
It transforms them into a continuous Gaussian space where relationships are easier to model.
Model dependencies
A multivariate Gaussian distribution is fit to the transformed data.
This captures how each variable depends on others (e.g., age ↔ BMI ↔ blood pressure).
Sample synthetic data
New synthetic samples are drawn from this learned distribution, then mapped back to the original data space.
Output verification
The generated dataset is post-processed and evaluated for correlation deltas, privacy risk, and consistency metrics.

Example

Let’s say your real dataset has these correlations:

Variable Pair	Correlation
Age ↔ BMI	0.42
BMI ↔ Systolic BP	0.61
Age ↔ Glucose	0.58

The synthetic dataset produced by VeriSynth will preserve these relationships closely, often within a correlation delta of ±0.1 — enough to be statistically realistic for most analytical and ML tasks.

Instead of random noise, GC learns your dataset’s statistical DNA and regenerates it faithfully.

Strengths

Feature	Description
Lightweight	Runs entirely on CPU — no GPUs required
Deterministic	Same seed → same result, ideal for reproducibility
Explainable	Transparent mathematical model (no black box)
Fast	Generates up to 1M+ rows in seconds
Auditable	Outputs proof receipts with correlation metrics

Limitations

Limitation	Description
Linear correlations only	GC struggles with nonlinear or multimodal relationships
Limited categorical complexity	High-cardinality categories may be oversimplified
No temporal or sequential modeling	Best for static, tabular datasets

Roadmap: Upcoming Models

VeriSynth Core is model-agnostic — future releases will expand into deep generative and privacy-enhanced models.

Model	Type	Status	Description
CTGAN	GAN-based tabular	🧩 Planned	Captures nonlinear relationships and rare categories
TVAE	Variational Autoencoder	🧩 Planned	Produces smoother synthetic distributions and uncertainty estimates
DP-GC	Differentially Private Gaussian Copula	🧩 Planned	Adds formal privacy bounds (ε) to correlation modeling
TimeSynth	Sequential/time-series	🧩 Planned	Models synthetic patient timelines, transactions, or sensor data
ImageSynth	Generative Vision	🚧 Exploratory	Extends proof-based synthesis to visual data

Model Selection Philosophy

We believe every synthetic data generator should be:

Understandable — transparent about how it learns and samples
Reproducible — deterministic seeds, reproducible metrics
Verifiable — accompanied by measurable fidelity and privacy proofs
Modular — easy to swap models as new techniques evolve

VeriSynth is built as a modular framework, so you can run:

verisynth data/patients.csv -o out/ --model gaussian_copula
# (future)
verisynth data/patients.csv -o out/ --model ctgan

Model Registry (Coming Soon)

We’re working on a model registry system that will let users:

View available models (verisynth models list)
Inspect metadata, dependencies, and required hardware
Register custom synthesis engines via plugin
Benchmark models on fidelity vs privacy

Example:

verisynth models list
# → gaussian_copula, ctgan, tvae, dp_gc

Model Validation Metrics

Each model is evaluated using:

Metric	Description
Correlation delta (Δ)	Measures difference in variable relationships
KS test (p-value)	Checks distributional similarity
Naive re-identification risk	Approximates privacy exposure
Synthetic utility	Performance of ML models trained on synthetic data

These metrics are logged to proof.json for transparency and reproducibility.

Example CLI Run (GC Model)

verisynth data/finance.csv -o out/ --rows 50000 --model gaussian_copula --seed 42

Produces:

📁 out/synthetic.csv
🧾 out/proof.json

With proof:

{
  "model": { "engine": "gaussian_copula", "seed": 42 },
  "metrics": { "corr_mean_abs_delta": 0.15, "naive_reid_risk": 0.0 }
}

Summary

Principle	Description
Start simple	GC gives accurate, transparent tabular synthesis
Build modularly	New models can plug into the same proof pipeline
Stay verifiable	Every model, no matter how complex, will always produce a proof receipt

​Models in VeriSynth

​Current Model: Gaussian Copula (GC)

​How It Works

​Example

​Strengths

​Limitations

​Roadmap: Upcoming Models

​Model Selection Philosophy

​Model Registry (Coming Soon)

​Model Validation Metrics

​Example CLI Run (GC Model)

​Summary