Vertical Federated Learning (vFL) allows multiple parties that own different attributes (e.g. features and labels) of the same data entity (e.g. a person) to jointly train a model. To prepare the training data, vFL needs to identify the common data entities shared by all parties. It is usually achieved by Private Set Intersection (PSI) which identifies the intersection of training samples from all parties by using personal identifiable information (e.g. email) as sample IDs to align data instances. As a result, PSI would make sample IDs of the intersection visible to all parties, and therefore each party can know that the data entities shown in the intersection also appear in the other parties, i.e. intersection membership. However, in many real-world privacy-sensitive organizations, e.g. banks and hospitals, revealing membership of their data entities is prohibited. In this paper, we propose a vFL framework based on Private Set Union (PSU) that allows each party to keep sensitive membership information to itself. Instead of identifying the intersection of all training samples, our PSU protocol generates the union of samples as training instances. In addition, we propose strategies to generate synthetic features and labels to handle samples that belong to the union but not the intersection. Through extensive experiments on two real-world datasets, we show our framework can protect the privacy of the intersection membership while maintaining the model utility.

中文翻译：

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不显示交叉成员身份的垂直联合学习

垂直联合学习 (vFL) 允许拥有同一数据实体（例如人）的不同属性（例如特征和标签）的多方联合训练模型。为了准备训练数据，vFL 需要识别各方共享的公共数据实体。它通常通过私有集交集（PSI）来实现，它通过使用个人身份信息（例如电子邮件）作为样本 ID 来对齐数据实例，从而识别来自各方的训练样本的交集。因此，PSI 将使所有参与方都可以看到交叉点的样本 ID，因此每一方都可以知道在交叉点中显示的数据实体也出现在其他方中，即交叉点成员资格。然而，在许多现实世界的隐私敏感组织中，例如银行和医院，禁止透露其数据实体的成员身份。在本文中，我们提出了一个基于私有集联合 (PSU) 的 vFL 框架，该框架允许每一方将敏感的成员信息保留给自己。我们的 PSU 协议不是识别所有训练样本的交集，而是生成样本的并集作为训练实例。此外，我们提出了生成合成特征和标签的策略，以处理属于联合但不属于交集的样本。通过对两个真实世界数据集的大量实验，我们表明我们的框架可以在保持模型效用的同时保护交叉点成员的隐私。我们的 PSU 协议不是识别所有训练样本的交集，而是生成样本的并集作为训练实例。此外，我们提出了生成合成特征和标签的策略，以处理属于联合但不属于交集的样本。通过对两个真实世界数据集的大量实验，我们表明我们的框架可以在保持模型效用的同时保护交叉点成员的隐私。我们的 PSU 协议不是识别所有训练样本的交集，而是生成样本的并集作为训练实例。此外，我们提出了生成合成特征和标签的策略，以处理属于联合但不属于交集的样本。通过对两个真实世界数据集的大量实验，我们表明我们的框架可以在保持模型效用的同时保护交叉点成员的隐私。