Quadratic Functional Encryption for Secure Training in Vertical Federated Learning

• URL: http://arxiv.org/abs/2305.08358v2
• Date: Mon, 19 Jun 2023 10:01:55 GMT
• Title: Quadratic Functional Encryption for Secure Training in Vertical Federated Learning
• Authors: Shuangyi Chen, Anuja Modi, Shweta Agrawal, Ashish Khisti
• Abstract summary: Vertical federated learning (VFL) enables the collaborative training of machine learning (ML) models in settings where the data is distributed amongst multiple parties. In VFL, the labels are available to a single party and the complete feature set is formed only when data from all parties is combined. Recently, Xu et al. proposed a new framework called FedV for secure gradient computation for VFL using multi-input functional encryption.
• Score: 26.188083606166806
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Vertical federated learning (VFL) enables the collaborative training of machine learning (ML) models in settings where the data is distributed amongst multiple parties who wish to protect the privacy of their individual data. Notably, in VFL, the labels are available to a single party and the complete feature set is formed only when data from all parties is combined. Recently, Xu et al. proposed a new framework called FedV for secure gradient computation for VFL using multi-input functional encryption. In this work, we explain how some of the information leakage in Xu et al. can be avoided by using Quadratic functional encryption when training generalized linear models for vertical federated learning.

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