---
title: "私密生成式贝叶斯自助法：通过分块策略实现不确定性报告的隐私保护"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2608.02480v1"
permalink: "https://feed.astralor.com/items/itm_5aea94f098bec48f4d9cc5a7"
published: "2026-08-03T16:47:14.000Z"
score: 81
channel: "ai"
tags: ["PGBB","不确定性量化","差分隐私","摊销推断","贝叶斯自助法","隐私保护"]
---

# 私密生成式贝叶斯自助法：通过分块策略实现不确定性报告的隐私保护

## AI 点评

> 这篇论文真正值得关注的地方在于它把差分隐私和贝叶斯自助法结合在了一起，而且不用假设数据生成模型，这在实际应用中太有用了。对于做隐私计算、统计发布或者需要给决策提供置信区间的AI工程师来说，PGBB提供了一种即插即用的新思路，值得仔细研读。

## 速览

随着AI系统更广泛地接触个人数据，保护统计答案及其不确定性的隐私至关重要。本文提出一种新的私密贝叶斯自助法（PGBB），通过将个体随机分组并赋予组级权重，隐藏个体贡献，强化差分隐私保障。利用摊销推断将私有学习与后验采样解耦，在训练时加入校准噪声，后续采样无需额外隐私或计算开销。理论证明提供差分隐私保证，并分析收敛性与后验差异。模拟及美国人口普查、出生体重数据应用显示，PGBB在不确定性量化上优于需要指定数据生成模型的私有贝叶斯方法。

## 关键点

- 提出PGBB方法，利用分块策略和组级权重实现贝叶斯自助法的隐私保护，避免为每个个体分配随机权重。
- 将摊销推断与差分隐私结合，训练阶段加入校准噪声，后续后验采样无需额外隐私预算。
- 理论分析了差分隐私保证、收敛性及与普通阻塞自助法的后验差异，并给出无数据调参方案。
- 在模拟和美国人口普查、出生体重数据上，PGBB优于需指定生成模型的私有贝叶斯方法。

## 全文

With AI systems gaining more access to individuals' information, it is important to protect privacy when reporting statistical answers. Equally important is to privatize the reporting of uncertainty in such answers. To this end, we adopt a Bayesian likelihood-free framework and make simulation from the posterior private. In particular, we propose a new private instantiation of the Bayesian bootstrap using a blocking strategy. Rather than assigning idiosyncratic random weights to each individual, we randomly group individuals and assign a single weight to each group. By concealing individuals' contributions within a group, we fortify differential privacy gates. We harness amortized inference that decouples private learning from posterior sampling. A push-forward map from observation weights to posterior samples is learned privately by adding calibrated noise during training. Subsequent posterior draws require no additional privacy and computation budget. We call the resulting method the Private Generative Bayesian Bootstrap (PGBB). We establish a differential privacy guarantee, analyze convergence to the non-private blocked-bootstrap target, and quantify the discrepancy between the ordinary and blocked Bayesian-bootstrap posteriors. In addition, we derive data-free tuning of the block Dirichlet concentration parameter that restores posterior dispersion asymptotically. We also show a single fit of PGBB can support a family of loss-based decision rules simultaneously without additional privacy cost. In simulations and in applications to U.S. Census returns to schooling and U.S. natality birthweight quantiles, PGBB gives competitive private uncertainty quantification and improves over private Bayesian alternatives that require a specified data-generating model in common settings.

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_5aea94f098bec48f4d9cc5a7