Hijack Vertical Federated Learning Models As One Party

• URL: http://arxiv.org/abs/2212.00322v2
• Date: Fri, 16 Feb 2024 03:17:40 GMT
• Title: Hijack Vertical Federated Learning Models As One Party
• Authors: Pengyu Qiu, Xuhong Zhang, Shouling Ji, Changjiang Li, Yuwen Pu, Xing Yang, Ting Wang
• Abstract summary: Vertical federated learning (VFL) is an emerging paradigm that enables collaborators to build machine learning models together in a distributed fashion. Existing VFL frameworks use cryptographic techniques to provide data privacy and security guarantees.
• Score: 43.095945038428404
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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