Private Generative Bootstrap via Blocking: Privacy-Preserving Uncertainty Quantification
Score
81提出新颖的隐私贝叶斯自助法，填补了无需生成模型的私有不确定性量化空白。
理论分析完善，包括差分隐私保证、收敛性和后验差异，实验设计全面。
方法可直接应用于统计发布和隐私保护AI系统，无需指定生成模型，实用性强。
隐私保护与不确定性量化是当前AI领域热点，论文贴合最新需求。
arXiv论文，作者来自知名机构，且有理论分析和实证支持，可信度较高。
Base 89.5 × T2 Source weight ×0.9 = 81
Curation line 70Selected
Why selected
Strong methodological novelty, rigorous theory, thorough experiments, and alignment with the active research area of private Bayesian inference.
AI commentary
「What makes this paper noteworthy is its fusion of differential privacy with Bayesian bootstrap without requiring a data-generating model, which is highly practical. For engineers working on privacy-preserving computation, statistical release, or AI decision-making with confidence intervals, PGBB offers a plug-and-play new approach that deserves a close read.」
Quick overview
As AI systems gain more access to personal data, protecting the privacy of statistical answers and their uncertainty is crucial. This paper proposes a new Private Generative Bayesian Bootstrap (PGBB) that randomly groups individuals and assigns a single weight to each group, concealing individual contributions to strengthen differential privacy. Amortized inference decouples private learning from posterior sampling, with calibrated noise added during training; subsequent posterior draws require no extra privacy or computation. Theoretical guarantees for differential privacy, convergence, and posterior discrepancy are established. Simulations and applications to U.S. Census returns and birthweight quantiles show PGBB yields competitive private uncertainty quantification, outperforming private Bayesian alternatives that require a specified data-generating model.
- Introduces PGBB, a privacy-preserving Bayesian bootstrap using blocking and group-level weights instead of individual random weights.
- Combines amortized inference with differential privacy: calibrated noise is added during training, enabling subsequent posterior sampling at no extra privacy cost.
- Provides theoretical guarantees on differential privacy, convergence, and discrepancy from the non-private blocked bootstrap, plus a data-free tuning strategy.
- Demonstrates competitive private uncertainty quantification in simulations and U.S. Census/natality data, outperforming model-based private Bayesian alternatives.
Full text
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