Analyzing the Differentially Private Theil-Sen Estimator for Simple Linear Regression
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866909134218067968 |
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| author | Sarathy, Jayshree Vadhan, Salil |
| author_facet | Sarathy, Jayshree Vadhan, Salil |
| contents | In this paper, we study differentially private point and confidence interval estimators for simple linear regression. Motivated by recent work that highlights the strong empirical performance of an algorithm based on robust statistics, DPTheilSen, we provide a rigorous, finite-sample analysis of its privacy and accuracy properties, offer guidance on setting hyperparameters, and show how to produce differentially private confidence intervals to accompany its point estimates. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2207_13289 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Analyzing the Differentially Private Theil-Sen Estimator for Simple Linear Regression Sarathy, Jayshree Vadhan, Salil Cryptography and Security Applications In this paper, we study differentially private point and confidence interval estimators for simple linear regression. Motivated by recent work that highlights the strong empirical performance of an algorithm based on robust statistics, DPTheilSen, we provide a rigorous, finite-sample analysis of its privacy and accuracy properties, offer guidance on setting hyperparameters, and show how to produce differentially private confidence intervals to accompany its point estimates. |
| title | Analyzing the Differentially Private Theil-Sen Estimator for Simple Linear Regression |
| topic | Cryptography and Security Applications |
| url | https://arxiv.org/abs/2207.13289 |