SecureSplit: Mitigating Backdoor Attacks in Split Learning
Fuente:
arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
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| _version_ | 1866908784919576576 |
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| author | Dou, Zhihao Cui, Dongfei Wang, Weida Gao, Anjun Quan, Yueyang Ma, Mengyao Vo, Viet Bai, Guangdong Liu, Zhuqing Fang, Minghong |
| author_facet | Dou, Zhihao Cui, Dongfei Wang, Weida Gao, Anjun Quan, Yueyang Ma, Mengyao Vo, Viet Bai, Guangdong Liu, Zhuqing Fang, Minghong |
| contents | Split Learning (SL) offers a framework for collaborative model training that respects data privacy by allowing participants to share the same dataset while maintaining distinct feature sets. However, SL is susceptible to backdoor attacks, in which malicious clients subtly alter their embeddings to insert hidden triggers that compromise the final trained model. To address this vulnerability, we introduce SecureSplit, a defense mechanism tailored to SL. SecureSplit applies a dimensionality transformation strategy to accentuate subtle differences between benign and poisoned embeddings, facilitating their separation. With this enhanced distinction, we develop an adaptive filtering approach that uses a majority-based voting scheme to remove contaminated embeddings while preserving clean ones. Rigorous experiments across four datasets (CIFAR-10, MNIST, CINIC-10, and ImageNette), five backdoor attack scenarios, and seven alternative defenses confirm the effectiveness of SecureSplit under various challenging conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_14054 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SecureSplit: Mitigating Backdoor Attacks in Split Learning Dou, Zhihao Cui, Dongfei Wang, Weida Gao, Anjun Quan, Yueyang Ma, Mengyao Vo, Viet Bai, Guangdong Liu, Zhuqing Fang, Minghong Cryptography and Security Distributed, Parallel, and Cluster Computing Machine Learning Split Learning (SL) offers a framework for collaborative model training that respects data privacy by allowing participants to share the same dataset while maintaining distinct feature sets. However, SL is susceptible to backdoor attacks, in which malicious clients subtly alter their embeddings to insert hidden triggers that compromise the final trained model. To address this vulnerability, we introduce SecureSplit, a defense mechanism tailored to SL. SecureSplit applies a dimensionality transformation strategy to accentuate subtle differences between benign and poisoned embeddings, facilitating their separation. With this enhanced distinction, we develop an adaptive filtering approach that uses a majority-based voting scheme to remove contaminated embeddings while preserving clean ones. Rigorous experiments across four datasets (CIFAR-10, MNIST, CINIC-10, and ImageNette), five backdoor attack scenarios, and seven alternative defenses confirm the effectiveness of SecureSplit under various challenging conditions. |
| title | SecureSplit: Mitigating Backdoor Attacks in Split Learning |
| topic | Cryptography and Security Distributed, Parallel, and Cluster Computing Machine Learning |
| url | https://arxiv.org/abs/2601.14054 |