Functional mechanism : regression analysis under differential privacy
ɛ-differential privacy is the state-of-the-art model for releasing sensitive information while protecting privacy. Numerous methods have been proposed to enforce epsilon-differential privacy in various analytical tasks, e.g., regression analysis. Existing solutions for regression analysis, however,...
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| Main Authors: | , , , , |
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| 其他作者: | |
| 格式: | Article |
| 語言: | English |
| 出版: |
2014
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/102390 http://hdl.handle.net/10220/18929 http://arxiv.org/abs/1208.0219 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | ɛ-differential privacy is the state-of-the-art model for releasing sensitive information while protecting privacy. Numerous methods have been proposed to enforce epsilon-differential privacy in various analytical tasks, e.g., regression analysis. Existing solutions for regression analysis, however, are either limited to non-standard types of regression or unable to produce accurate regression results. Motivated by this, we propose the Functional Mechanism, a differentially private method designed for a large class of optimization-based analyses. The main idea is to enforce epsilon-differential privacy by perturbing the objective function of the optimization problem, rather than its results. As case studies, we apply the functional mechanism to address two most widely used regression models, namely, linear regression and logistic regression. Both theoretical analysis and thorough experimental evaluations show that the functional mechanism is highly effective and efficient, and it significantly outperforms existing solutions. |
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