Ensembler: Protect Collaborative Inference Privacy from Model Inversion Attack via Selective Ensemble
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929642888232960 |
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| author | Liu, Dancheng Xu, Chenhui Li, Jiajie Nassereldine, Amir Xiong, Jinjun |
| author_facet | Liu, Dancheng Xu, Chenhui Li, Jiajie Nassereldine, Amir Xiong, Jinjun |
| contents | For collaborative inference through a cloud computing platform, it is sometimes essential for the client to shield its sensitive information from the cloud provider. In this paper, we introduce Ensembler, an extensible framework designed to substantially increase the difficulty of conducting model inversion attacks by adversarial parties. Ensembler leverages selective model ensemble on the adversarial server to obfuscate the reconstruction of the client's private information. Our experiments demonstrate that Ensembler can effectively shield input images from reconstruction attacks, even when the client only retains one layer of the network locally. Ensembler significantly outperforms baseline methods by up to 43.5% in structural similarity while only incurring 4.8% time overhead during inference. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_10859 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Ensembler: Protect Collaborative Inference Privacy from Model Inversion Attack via Selective Ensemble Liu, Dancheng Xu, Chenhui Li, Jiajie Nassereldine, Amir Xiong, Jinjun Cryptography and Security Machine Learning For collaborative inference through a cloud computing platform, it is sometimes essential for the client to shield its sensitive information from the cloud provider. In this paper, we introduce Ensembler, an extensible framework designed to substantially increase the difficulty of conducting model inversion attacks by adversarial parties. Ensembler leverages selective model ensemble on the adversarial server to obfuscate the reconstruction of the client's private information. Our experiments demonstrate that Ensembler can effectively shield input images from reconstruction attacks, even when the client only retains one layer of the network locally. Ensembler significantly outperforms baseline methods by up to 43.5% in structural similarity while only incurring 4.8% time overhead during inference. |
| title | Ensembler: Protect Collaborative Inference Privacy from Model Inversion Attack via Selective Ensemble |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2401.10859 |