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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.17042 |
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| _version_ | 1866929428410400768 |
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| author | He, Ying Niu, Mingyang Hua, Jingyu Mao, Yunlong Huang, Xu Li, Chen Zhong, Sheng |
| author_facet | He, Ying Niu, Mingyang Hua, Jingyu Mao, Yunlong Huang, Xu Li, Chen Zhong, Sheng |
| contents | Split Neural Network, as one of the most common architectures used in vertical federated learning, is popular in industry due to its privacy-preserving characteristics. In this architecture, the party holding the labels seeks cooperation from other parties to improve model performance due to insufficient feature data. Each of these participants has a self-defined bottom model to learn hidden representations from its own feature data and uploads the embedding vectors to the top model held by the label holder for final predictions. This design allows participants to conduct joint training without directly exchanging data. However, existing research points out that malicious participants may still infer label information from the uploaded embeddings, leading to privacy leakage. In this paper, we first propose an embedding extension attack manipulating embeddings to undermine existing defense strategies, which rely on constraining the correlation between the embeddings uploaded by participants and the labels. Subsequently, we propose a new label obfuscation defense strategy, called `LabObf', which randomly maps each original integer-valued label to multiple real-valued soft labels with values intertwined, significantly increasing the difficulty for attackers to infer the labels. We conduct experiments on four different types of datasets, and the results show that LabObf significantly reduces the attacker's success rate compared to raw models while maintaining desirable model accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_17042 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LabObf: A Label Protection Scheme for Vertical Federated Learning Through Label Obfuscation He, Ying Niu, Mingyang Hua, Jingyu Mao, Yunlong Huang, Xu Li, Chen Zhong, Sheng Machine Learning Cryptography and Security Split Neural Network, as one of the most common architectures used in vertical federated learning, is popular in industry due to its privacy-preserving characteristics. In this architecture, the party holding the labels seeks cooperation from other parties to improve model performance due to insufficient feature data. Each of these participants has a self-defined bottom model to learn hidden representations from its own feature data and uploads the embedding vectors to the top model held by the label holder for final predictions. This design allows participants to conduct joint training without directly exchanging data. However, existing research points out that malicious participants may still infer label information from the uploaded embeddings, leading to privacy leakage. In this paper, we first propose an embedding extension attack manipulating embeddings to undermine existing defense strategies, which rely on constraining the correlation between the embeddings uploaded by participants and the labels. Subsequently, we propose a new label obfuscation defense strategy, called `LabObf', which randomly maps each original integer-valued label to multiple real-valued soft labels with values intertwined, significantly increasing the difficulty for attackers to infer the labels. We conduct experiments on four different types of datasets, and the results show that LabObf significantly reduces the attacker's success rate compared to raw models while maintaining desirable model accuracy. |
| title | LabObf: A Label Protection Scheme for Vertical Federated Learning Through Label Obfuscation |
| topic | Machine Learning Cryptography and Security |
| url | https://arxiv.org/abs/2405.17042 |