Differentially Private Non Parametric Copulas: Generating Synthetic Data with Non Parametric Copulas Under Privacy Guarantees

Creation of models to generate synthetic data has represented a significant advancement across diverse scientific fields, but this technology also brings important privacy considerations for users. This work focuses on enhancing a nonparametric copula-based synthetic data generation model, DPNPC, by incorporating Differential Privacy through an Enhanced Fourier Perturbation method. The model generates synthetic data for mixed tabular databases while preserving privacy. We compare DP-NPC with three other models (PrivBayes, DP-Copula, and DP-Histogram) across three public datasets (income with sociodemographic information, criminal defendant's likelihood data, and Hospital data considering utilization, charity, and admission data), evaluating privacy, utility, and execution time. DP-NPC outperforms others in modeling multivariate dependencies, maintaining privacy for small <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\epsilon$</tex> values, and reducing training times. However, limitations include the need to assess the model's performance with different encoding methods for categorical variables and consider additional privacy attacks. Future research should address these areas to enhance privacypreserving synthetic data generation.