Quickstart

Let’s generate your first verifiable synthetic dataset — right from your terminal. VeriSynth runs 100% locally, needs no GPU, and produces both a synthetic dataset and a cryptographic proof receipt for auditability.

1. Install VeriSynth Core

pip install verisynth-core

That’s all you need. VeriSynth ships as a self-contained Python package with zero external dependencies beyond standard data libraries.

2. Prepare your dataset

Start with any CSV file — for example, a small tabular dataset of patients, transactions, or IoT readings.

data/patients.csv

Example data:

patient_id	age	bmi	smoker	hba1c	systolic_bp	diastolic_bp
001	62	31.4	1	7.8	148	90
002	45	28.6	0	6.1	132	82
003	33	24.1	0	5.3	116	74

You can also grab sample data here: https://github.com/VeriSynthAI/verisynth-core/tree/main/data

3. Generate synthetic data

Run VeriSynth directly from the CLI:

verisynth data/patients.csv -o out/ --rows 1000000 --seed 42

✅ This will produce two files:

📁 out/synthetic.csv   # Synthetic dataset
🧾 out/proof.json      # Cryptographic proof receipt

4. Inspect the proof receipt

Each run creates a verifiable JSON proof describing:

Dataset hashes (SHA-256)
Model configuration (Gaussian Copula)
Random seed (for reproducibility)
Privacy and correlation metrics
Merkle root for integrity checking

Example:

{
  "verisynth_version": "core-0.1.0",
  "metrics": { "corr_mean_abs_delta": 0.12, "naive_reid_risk": 0.01 },
  "input":  { "rows": 10, "sha256": "…82b7" },
  "output": { "rows": 1000000, "sha256": "…acb9" },
  "proof": "merkle_root: …c31"
}

5. Explore your synthetic dataset

The generated data preserves the statistical structure of your original dataset — correlations, distributions, and realistic relationships — without containing any real records. Try viewing it in pandas:

import pandas as pd
df = pd.read_csv("out/synthetic.csv")
df.head()

6. Reproduce your results

Because VeriSynth uses deterministic seeding (--seed), you can always reproduce the exact same synthetic dataset and proof receipt from the same input file.

verisynth data/patients.csv -o out/ --rows 1000000 --seed 42

Running this command again, with the same seed value will yield identical Merkle roots and metrics, proving the process is verifiable and consistent.

​Quickstart

​1. Install VeriSynth Core

​2. Prepare your dataset

​3. Generate synthetic data

​4. Inspect the proof receipt

​5. Explore your synthetic dataset

​6. Reproduce your results