Shuffle模型中来自虚假用户的差异私有直方图
Differentially Private Histograms in the Shuffle Model from Fake Users
摘要：
最近有很多工作在差分隐私的洗牌模型，特别是近似$d$ -bin直方图。虽然这些协议实现了低错误，但每个用户发送的消息数量（消息复杂度）到目前为止已随$d$或隐私参数而缩放。消息复杂度是混洗协议的资源消耗的信息预测器。我们提出了一个协议，其消息的复杂性是2时，有足够多的用户。该协议基本上将数据集中的每一行与一个假行配对，并对所有行执行简单的随机化。我们表明，该协议引入的错误是小的，使用严格的分析，以及对真实世界的数据进行实验。我们还证明，腐败的用户有一个相对较低的影响，我们的协议的估计。
英文摘要：
There has been much recent work in the shuffle model of differential privacy, particularly for approximate $d$-bin histograms. While these protocols achieve low error, the number of messages sent by each user -- the message complexity -- has so far scaled with $d$ or the privacy parameters. The message complexity is an informative predictor of a shuffle protocol's resource consumption. We present a protocol whose message complexity is two when there are sufficiently many users. The protocol essentially pairs each row in the dataset with a fake row and performs a simple randomization on all rows. We show that the error introduced by the protocol is small, using rigorous analysis as well as experiments on real-world data. We also prove that corrupt users have a relatively low impact on our protocol's estimates.
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