Hijack Vertical Federated Learning Models As One Party

Authors: Pengyu Qiu, Xuhong Zhang, Shouling Ji, Changjiang Li, Yuwen Pu, Xing Yang, Ting Wang
Published: 2022-12-01 | Updated: 2024-02-16

Source: https://arxiv.org/abs/2212.00322

PDF: https://arxiv.org/pdf/2212.00322

AIにより推定されたラベル

敵対的サンプル 最適化問題 未ターゲット毒性攻撃

※ こちらのラベルはAIによって自動的に追加されました。そのため、正確でないことがあります。
詳細は文献データベースについてをご覧ください。

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.