提出可适配高维非共轭模型的噪声感知贝叶斯推断方法。
Noise-Aware Differentially Private Variational Inference
- 基于随机梯度变分推断，融合差分隐私扰动机制。
- 在高维线性回归中实现准确覆盖率，逻辑回归预测概率校准良好。
- 适用于复杂模型，且评估更精准，适合隐私敏感场景研究者。
差分隐私（DP）为统计推断提供强隐私保障，但可能导致下游应用结果不可靠并引入偏差。尽管已有若干噪声感知方法将DP扰动融入推断过程，但仅限于特定类型的简单概率模型。本文提出一种基于随机梯度变分推断的新方法，可推广至高维及非共轭模型。同时提出更精确的噪声感知后验评估方法。实验表明，在适用领域内性能与现有方法相当；在该范围外，高维贝叶斯线性回归中获得准确覆盖率，使用UCI Adult数据集的贝叶斯逻辑回归实现校准良好的预测概率。
原文摘要 · Abstract (English)
Differential privacy (DP) provides robust privacy guarantees for statistical inference, but this can lead to unreliable results and biases in downstream applications. While several noise-aware approaches have been proposed which integrate DP perturbation into the inference, they are limited to specific types of simple probabilistic models. In this work, we propose a novel method for noise-aware approximate Bayesian inference based on stochastic gradient variational inference which can also be applied to high-dimensional and non-conjugate models. We also propose a more accurate evaluation method for noise-aware posteriors. Empirically, our inference method has similar performance to existing methods in the domain where they are applicable. Outside this domain, we obtain accurate coverages on high-dimensional Bayesian linear regression and well-calibrated predictive probabilities on Bayesian logistic regression with the UCI Adult dataset.
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