用洗牌差分隐私实现用户数据的私密密度估计与分类。
Learning from End User Data with Shuffled Differential Privacy over Kernel Densities
- 用户本地加密后共享数据，通过洗牌增强隐私保护。
- 密度估计精度接近中心化差分隐私，可支持高精度分类。
- 无需暴露原始数据即可恢复类别语义，适合实际部署。
我们研究了在终端用户间分布的私密数据收集与学习问题。在洗牌差分隐私（shuffled DP）模型中，用户在共享前对数据进行局部保护，并在收集过程中进一步匿名化以提升隐私性。该模型成为中心化DP（需完全信任中央管理者）和本地DP（严重损害准确率）的有力替代方案。本文提出一种用于私密估计分布式数据核密度函数的洗牌差分隐私协议，其精度几乎等同于中心化差分隐私。利用该方法，可通过每类学习私密密度函数来训练分类器。此外，即使在无原始数据暴露的情况下，密度函数仍能恢复其所属类别的语义内容。实验表明，该方法在下游任务中表现优异，揭示了实际机器学习部署中洗牌差分隐私的关键考量与权衡。
原文摘要 · Abstract (English)
We study a setting of collecting and learning from private data distributed across end users. In the shuffled model of differential privacy, the end users partially protect their data locally before sharing it, and their data is also anonymized during its collection to enhance privacy. This model has recently become a prominent alternative to central DP, which requires full trust in a central data curator, and local DP, where fully local data protection takes a steep toll on downstream accuracy. Our main technical result is a shuffled DP protocol for privately estimating the kernel density function of a distributed dataset, with accuracy essentially matching central DP. We use it to privately learn a classifier from the end user data, by learning a private density function per class. Moreover, we show that the density function itself can recover the semantic content of its class, despite having been learned in the absence of any unprotected data. Our experiments show the favorable downstream performance of our approach, and highlight key downstream considerations and trade-offs in a practical ML deployment of shuffled DP.
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