用差分隐私保护数据生成，让合成数据更安全可靠。
Differentially Private Non Parametric Copulas: Generating synthetic data with non parametric copulas under privacy guarantees
- 基于非参数耦合模型，结合傅里叶扰动实现差分隐私
- 在小ε值下仍保持高隐私性，训练速度更快
- 适合需要高隐私保障的医疗、金融数据合成
合成数据生成技术在多个科学领域取得显著进展，但用户隐私问题不容忽视。本文针对非参数耦合模型DPNPC，通过增强傅里叶扰动方法引入差分隐私，实现对混合表格数据的合成。在三个公开数据集上，与PrivBayes、DP-Copula和DP-Histogram模型对比，DPNPC在建模多变量依赖关系、小ε值下的隐私保护能力以及训练时间方面均表现更优。然而，模型性能受编码方式影响，且需进一步评估其他隐私攻击。未来研究应关注编码策略优化与更全面的隐私验证。
原文摘要 · Abstract (English)
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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