Vertical Federated Learning without Revealing Intersection Membership

by   Jiankai Sun, et al.

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.


page 1

page 2

page 3

page 4


Incentive Allocation in Vertical Federated Learning Based on Bankruptcy Problem

Vertical federated learning (VFL) is a promising approach for collaborat...

Interpret Federated Learning with Shapley Values

Federated Learning is introduced to protect privacy by distributing trai...

Asymmetrical Vertical Federated Learning

Federated learning is a distributed machine learning method that aims to...

ActShare: Sensitive Data Sharing with Reliable Leaker Identification

Data sharing among multiple parties becomes increasingly common today, s...

PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN

We introduce PyVertical, a framework supporting vertical federated learn...

Private Membership Aggregation

We consider the problem of private membership aggregation (PMA), in whic...

BlindSage: Label Inference Attacks against Node-level Vertical Federated Graph Neural Networks

Federated learning enables collaborative training of machine learning mo...

Please sign up or login with your details

Forgot password? Click here to reset