Running a randomized algorithm on a subsampled dataset instead of the entire dataset amplifies differential privacy guarantees. In this work, in a federated setting, we consider random participation of the clients in addition to subsampling their local datasets. Since such random participation of the clients creates correlation among the samples of the same client in their subsampling, we analyze the corresponding privacy amplification via non-uniform subsampling. We show that when the size of the local datasets is small, the privacy guarantees via random participation is close to those of the centralized setting, in which the entire dataset is located in a single host and subsampled. On the other hand, when the local datasets are large, observing the output of the algorithm may disclose the identities of the sampled clients with high confidence. Our analysis reveals that, even in this case, privacy guarantees via random participation outperform those via only local subsampling.

翻译：在子抽样数据集而不是整个数据集上运行随机算法会放大不同的隐私保障。 在这项工作中, 在联合环境下, 我们考虑客户随机参与, 除了对本地数据集进行子抽样外, 我们还考虑客户随机参与。 由于客户的这种随机参与在子抽样中产生了同一个客户样本的关联性, 我们通过非统一的子抽样分析相应的隐私放大。 我们显示, 当本地数据集规模小时, 通过随机参与的隐私保障接近集中环境中的隐私保障, 整个数据集都位于单个主机和子抽样中。 另一方面, 当本地数据集大时, 观察算法的输出可能会以高度信任的方式披露抽样客户的身份 。 我们的分析显示, 即使在此情况下, 通过随机参与的隐私保障仅通过本地子抽样来显示隐私。