Differentially Private Iterative Screening Rules for Linear Regression
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912247216865280 |
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| author | Khanna, Amol Lu, Fred Raff, Edward |
| author_facet | Khanna, Amol Lu, Fred Raff, Edward |
| contents | Linear $L_1$-regularized models have remained one of the simplest and most effective tools in data science. Over the past decade, screening rules have risen in popularity as a way to eliminate features when producing the sparse regression weights of $L_1$ models. However, despite the increasing need of privacy-preserving models for data analysis, to the best of our knowledge, no differentially private screening rule exists. In this paper, we develop the first private screening rule for linear regression. We initially find that this screening rule is too strong: it screens too many coefficients as a result of the private screening step. However, a weakened implementation of private screening reduces overscreening and improves performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_18578 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Differentially Private Iterative Screening Rules for Linear Regression Khanna, Amol Lu, Fred Raff, Edward Machine Learning Artificial Intelligence Cryptography and Security Linear $L_1$-regularized models have remained one of the simplest and most effective tools in data science. Over the past decade, screening rules have risen in popularity as a way to eliminate features when producing the sparse regression weights of $L_1$ models. However, despite the increasing need of privacy-preserving models for data analysis, to the best of our knowledge, no differentially private screening rule exists. In this paper, we develop the first private screening rule for linear regression. We initially find that this screening rule is too strong: it screens too many coefficients as a result of the private screening step. However, a weakened implementation of private screening reduces overscreening and improves performance. |
| title | Differentially Private Iterative Screening Rules for Linear Regression |
| topic | Machine Learning Artificial Intelligence Cryptography and Security |
| url | https://arxiv.org/abs/2502.18578 |