Knockoffs Inference under Privacy Constraints
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916790636904448 |
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| author | Cai, Zhanrui Fan, Yingying Gao, Lan |
| author_facet | Cai, Zhanrui Fan, Yingying Gao, Lan |
| contents | Model-X knockoff framework offers a model-free variable selection method that ensures finite sample false discovery rate (FDR) control. However, the complexity of generating knockoff variables, coupled with the model-free assumption, presents significant challenges for protecting data privacy in this context. In this paper, we propose a comprehensive framework for knockoff inference within the differential privacy paradigm. Our proposed method guarantees robust privacy protection while preserving the exact FDR control entailed by the original model-X knockoff procedure. We further conduct power analysis and establish sufficient conditions under which the noise added for privacy preservation does not asymptotically compromise power. Through various applications, we demonstrate that the differential privacy knockoff (DP-knockoff) method can be effectively utilized to safeguard privacy during variable selection with FDR control in both low and high dimensional settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_09690 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Knockoffs Inference under Privacy Constraints Cai, Zhanrui Fan, Yingying Gao, Lan Methodology Machine Learning Model-X knockoff framework offers a model-free variable selection method that ensures finite sample false discovery rate (FDR) control. However, the complexity of generating knockoff variables, coupled with the model-free assumption, presents significant challenges for protecting data privacy in this context. In this paper, we propose a comprehensive framework for knockoff inference within the differential privacy paradigm. Our proposed method guarantees robust privacy protection while preserving the exact FDR control entailed by the original model-X knockoff procedure. We further conduct power analysis and establish sufficient conditions under which the noise added for privacy preservation does not asymptotically compromise power. Through various applications, we demonstrate that the differential privacy knockoff (DP-knockoff) method can be effectively utilized to safeguard privacy during variable selection with FDR control in both low and high dimensional settings. |
| title | Knockoffs Inference under Privacy Constraints |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/2506.09690 |