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arXiv · 2212.00322

Hijack Vertical Federated Learning Models As One Party

Pengyu Qiu·Xuhong Zhang·Shouling Ji·Changjiang Li·Yuwen Pu·Xing Yang·Ting Wang
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Abstract

Vertical federated learning (VFL) is an emerging paradigm that enables collaborators to build machine learning models together in a distributed fashion. In general, these parties have a group of users in common but own different features. Existing VFL frameworks use cryptographic techniques to provide data privacy and security guarantees, leading to a line of works studying computing efficiency and fast implementation. However, the security of VFL's model remains underexplored.

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Pengyu Qiu, Xuhong Zhang, Shouling Ji, Changjiang Li, Yuwen Pu, Xing Yang, Ting Wang. 2022-12-01. Hijack Vertical Federated Learning Models As One Party. https://doi.org/10.1109/tdsc.2024.3358081

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