Summary of Differentially Private Non Parametric Copulas: Generating Synthetic Data with Non Parametric Copulas Under Privacy Guarantees, by Pablo A. Osorio-marulanda et al.
Differentially Private Non Parametric Copulas: Generating synthetic data with non parametric copulas under privacy guarantees
by Pablo A. Osorio-Marulanda, John Esteban Castro Ramirez, Mikel Hernández Jiménez, Nicolas Moreno Reyes, Gorka Epelde Unanue
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Databases (cs.DB)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper enhances a non-parametric 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. Compared to three other models (PrivBayes, DP-Copula, and DP-Histogram) across three public datasets, DPNPC outperforms others in modeling multivariate dependencies, maintaining privacy for small epsilon values, and reducing training times. However, limitations include the need to assess the model’s performance with different encoding methods and consider additional privacy attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a special kind of fake data that keeps people’s private information safe. They improved an existing method called DPNPC by adding extra protection called Differential Privacy. This new method is better than others at creating fake data that looks like real data and keeps it private. It also takes less time to make the fake data. However, there are still some things they need to work on to make sure their method is even better. |
Keywords
» Artificial intelligence » Synthetic data
