Vertical Federated Learning without Revealing Intersection Membership

• URL: http://arxiv.org/abs/2106.05508v1
• Date: Thu, 10 Jun 2021 05:26:50 GMT
• Title: Vertical Federated Learning without Revealing Intersection Membership
• Abstract summary: Vertical Federated Learning (vFL) allows multiple parties that own different attributes of the same data entity to jointly train a model. It is usually achieved by Private Set Intersection (PSI) which identifies the intersection of training samples from all parties. We propose a vFL framework based on Private Set Union (PSU) that allows each party to keep sensitive membership information to itself.
• Score: 30.612650238615267
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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