Federated learning (FL) allows multiple parties, each holding a dataset, to jointly train a model without leaking any information about their own datasets. In this paper, we focus on vertical FL (VFL). In VFL, each party holds a dataset with the same sample space and different feature spaces. All parties should first agree on the training dataset in the ID alignment phase. However, existing works may leak some information about the training dataset and cause privacy leakage. To address this issue, this paper proposes OpenVFL, a vertical federated learning framework with stronger privacy-preserving. We first propose NCLPSI, a new variant of labeled PSI, in which both parties can invoke this protocol to get the encrypted training dataset without leaking any additional information. After that, both parties train the model over the encrypted training dataset. We also formally analyze the security of OpenVFL. In addition, the experimental results show that OpenVFL achieves the best trade-offs between accuracy, performance, and privacy among the most state-of-the-art works.

中文翻译：

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OpenVFL：具有更强隐私保护的垂直联合学习框架


联合学习 （FL） 允许多方（每方都持有一个数据集）联合训练模型，而不会泄露有关他们自己的数据集的任何信息。在本文中，我们重点介绍垂直 FL （VFL）。在 VFL 中，每一方都持有一个具有相同样本空间和不同特征空间的数据集。各方应首先在 ID 对齐阶段就训练数据集达成一致。但是，现有的工作可能会泄露一些关于训练数据集的信息，并导致隐私泄露。为了解决这个问题，本文提出了 OpenVFL，这是一种具有更强隐私保护能力的垂直联邦学习框架。我们首先提出了 NCLPSI，这是标记 PSI 的一种新变体，其中双方都可以调用此协议来获取加密的训练数据集，而不会泄露任何附加信息。之后，双方在加密的训练数据集上训练模型。我们还正式分析了 OpenVFL 的安全性。此外，实验结果表明，OpenVFL 在最先进的作品中实现了准确性、性能和隐私性之间的最佳权衡。