dpsdg 0.1.1

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Differentially Private Synthetic Data Generation (DPSDG)

This repository contains the official implementation of the paper "Measuring Privacy Risks and Tradeoffs in Financial Synthetic Data Generation" as seen in TIME workshop of WebConf 2026. The code implements the same specs as the original CTGAN/TVAE model from the ctgan package.

Installation

From PyPI

pip install dpsdg

From Source

Clone the repository and install it locally:

git clone https://github.com/brains-group/dpsdg.git
cd dpsdg
pip install .

Or install directly from GitHub without cloning first:

pip install git+https://github.com/brains-group/dpsdg.git

Usage

This package extends CTGAN with differentially private training via DP-SGD. The API closely mirrors the original, so the only change is swapping the model class and adding a few privacy parameters.

Your data should be a pandas DataFrame with:

• Continuous columns as float
• Discrete/categorical columns as int or str
• No missing values

DP-CTGAN

Original CTGAN (no privacy):

from ctgan import CTGAN

ctgan = CTGAN(epochs=300)
ctgan.fit(real_data, discrete_columns)
synthetic_data = ctgan.sample(1000)

DP-CTGAN (with differential privacy):

from dpsdg.models.dp_ctgan import IterDPCTGAN

model = IterDPCTGAN(epsilon=1.0, delta=1e-5, epochs=300)
model.fit_transformer(real_data, discrete_columns)  # must be called before fit
model.fit(real_data, discrete_columns)
synthetic_data = model.sample(1000)

The key difference is the fit_transformer call before fit. This sets up the privacy-aware data transformer. After that, fit and sample work exactly as in the original.

DP-TVAE

Original TVAE (no privacy):

from ctgan import TVAE

tvae = TVAE(epochs=300)
tvae.fit(real_data, discrete_columns)
synthetic_data = tvae.sample(1000)

DP-TVAE (with differential privacy):

from dpsdg.models.dp_tvae import IterTVAE

model = IterTVAE(epsilon=1.0, delta=1e-5, epochs=300)
model.fit(real_data, discrete_columns)
synthetic_data = model.sample(1000)

Privacy Parameters

Both models add DP-specific parameters on top of the standard CTGAN/TVAE arguments.

Shared Parameters

Parameter	Type	Default	Description
epsilon	float	0.0 (CTGAN) / 1.0 (TVAE)	Privacy budget (ε). Smaller values give a stronger privacy guarantee at the cost of data utility. Setting epsilon=0.0 disables DP noise entirely. Common choices are 1.0, 5.0, or 10.0.
delta	float	1e-5	Failure probability (δ) for the DP guarantee. Should be much smaller than 1/n, where n is the number of training rows.
max_grad_norm	float	1.0	Per-sample gradient clipping threshold used in DP-SGD. This bounds the sensitivity of each update — larger values preserve more gradient signal but require proportionally more noise to achieve the same ε.

DP-CTGAN Only

Parameter	Type	Default	Description
use_gradient_penalty	bool	True	Enables a WGAN-GP style gradient penalty on the discriminator. Recommended to keep enabled, as it stabilizes GAN training under the high noise levels typical of DP.
gp_lambda	float	10	Weight for the gradient penalty term in the discriminator loss. Higher values enforce the Lipschitz constraint more strongly.

DP-TVAE Only

Parameter	Type	Default	Description
use_opacus_noise_mul	bool	False	When True, delegates noise multiplier calculation to Opacus. By default the package computes it directly via opacus.accountants.utils.get_noise_multiplier.