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Differentially Private Non Parametric Copulas: Generating synthetic data with non parametric copulas under privacy guarantees

arXiv 2409.18611 ↗First posted 27 Sep 2024cs.LG cs.DBcited by 0

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Abstract (arXiv)

Creation of synthetic data models 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 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. We compare DPNPC with three other models (PrivBayes, DP-Copula, and DP-Histogram) across three public datasets, evaluating privacy, utility, and execution time. DPNPC outperforms others in modeling multivariate dependencies, maintaining privacy for small ε 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. Future research should address these areas to enhance privacy-preserving synthetic data generation.

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References · 13

  1. 2018The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networkscited by 847
  2. 2019Modeling Tabular data using Conditional GANcited by 695
  3. 2017Machine Learning Models that Remember Too Muchcited by 165
  4. 2020DP-MERF: Differentially Private Mean Embeddings with Random Features for Practical Privacy-Preserving Data Generationcited by 78
  5. 2022A Unified Framework for Quantifying Privacy Risk in Synthetic Datacited by 45
  6. 2020Differentially Private Synthetic Data: Applied Evaluations and Enhancementscited by 36
  7. 2019Synthetic Examples Improve Generalization for Rare Classescited by 35
  8. 2019From GAN to WGANcited by 34
  9. 2021Finding needles in a haystack: Sampling Structurally-diverse Training Sets from Synthetic Data for Compositional Generalizationcited by 27
  10. 2020Context based Text-generation using LSTM networkscited by 10
  11. 2022Privacy-Preserving Synthetic Educational Data Generationcited by 9
  12. 2022Generating synthetic mobility data for a realistic population with RNNs to improve utility and privacycited by 6
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  1. 2023Synthetic Demographic Data Generation for Card Fraud Detection Using GANscited by 1

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