Imitative Membership Inference Attack
Fuente:
arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866914244016996352 |
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| author | Du, Yuntao Chen, Yuetian Xiao, Hanshen Ribeiro, Bruno Li, Ninghui |
| author_facet | Du, Yuntao Chen, Yuetian Xiao, Hanshen Ribeiro, Bruno Li, Ninghui |
| contents | A Membership Inference Attack (MIA) assesses how much a target machine learning model reveals about its training data by determining whether specific query instances were part of the training set. State-of-the-art MIAs rely on training hundreds of shadow models that are independent of the target model, leading to significant computational overhead. In this paper, we introduce Imitative Membership Inference Attack (IMIA), which employs a novel imitative training technique to strategically construct a small number of target-informed imitative models that closely replicate the target model's behavior for inference. Extensive experimental results demonstrate that IMIA substantially outperforms existing MIAs in various attack settings while only requiring less than 5% of the computational cost of state-of-the-art approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_06796 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Imitative Membership Inference Attack Du, Yuntao Chen, Yuetian Xiao, Hanshen Ribeiro, Bruno Li, Ninghui Cryptography and Security Machine Learning A Membership Inference Attack (MIA) assesses how much a target machine learning model reveals about its training data by determining whether specific query instances were part of the training set. State-of-the-art MIAs rely on training hundreds of shadow models that are independent of the target model, leading to significant computational overhead. In this paper, we introduce Imitative Membership Inference Attack (IMIA), which employs a novel imitative training technique to strategically construct a small number of target-informed imitative models that closely replicate the target model's behavior for inference. Extensive experimental results demonstrate that IMIA substantially outperforms existing MIAs in various attack settings while only requiring less than 5% of the computational cost of state-of-the-art approaches. |
| title | Imitative Membership Inference Attack |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2509.06796 |