DPSynth

Differentially Private Synthetic Data with Guaranteed Utility

R package implementing differentially private (DP) synthetic data generation for tabular data, with standardized utility evaluation and empirical disclosure risk auditing.

Authors: Mukul Bijalwan (mukulbijalwan555@gmail.com), Gunjan Aggarwal, Mukul Jain

Features

DP-Copula synthesizer (dp_copula_synth) — DP histogram marginals + Gaussian-copula dependence with noisy rank correlations projected to the positive-definite cone. Handles mixed numeric/categorical data.
DP-GMM (dp_gmm_synth) — Gaussian mixture models fit via EM on perturbed sufficient statistics (noisy counts, means, variances), DP k-means++ initialization through the exponential mechanism.
DP marginals (dp_marginals_synth) — fast per-column DP histogram baseline.
PATE (dp_pate_synth) — Private Aggregation of Teacher Ensembles for discrete / mixed-type synthesis.
Utility evaluation (evaluate_utility) — univariate (KS, Hellinger), multivariate (correlation distance, MI), propensity-score pMSE (Woo et al., 2009), downstream TSTR performance.
Risk auditing — membership inference, attribute disclosure, record linkage.
Budget accounting (new_synth_budget, spend_synth) — basic, advanced and Rényi-DP composition.

Installation

# install.packages("remotes")
remotes::install_github("MukulBijalwan/DPSynth")

Quick start

library(DPSynth)

data(adult_sample)
set.seed(1)
res <- dp_synthesize(adult_sample, method = "copula",
                     epsilon = 2.0, delta = 1e-6, n_synth = 1000)
print(res)
head(res$synthetic_data)

Privacy model

A generator G is (ε, δ)-DP if for all neighbouring datasets D ~ D′:

Pr[G(D) = S] ≤ e^ε · Pr[G(D′) = S] + δ

The entire synthetic dataset is the output; DPSynth privatizes the model parameters with calibrated noise, after which sampling is post-processing and consumes no additional budget.

Note on formal guarantees