Functional Mechanism: Regression Analysis under Differential Privacy
Functional Mechanism: Regression Analysis under Differential Privacy
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DOI：
10.14778/2350229.2350253
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发表时间：
2012-07-01
影响因子：
2.5
通讯作者：
Winslett, Marianne
中科院分区：
文献类型：
--
作者：
Zhang, Jun;Zhang, Zhenjie;Winslett, Marianne
ε-差分隐私是用于在保护隐私的同时发布敏感信息的最新模型。已经提出了许多方法来在各种分析任务中实施ε差分隐私，例如，回归分析然而，现有的回归分析解决方案要么局限于非标准类型的回归，要么不能产生准确的回归结果。出于这一动机，我们提出了功能机制，一个差异化的私有方法设计的一大类优化为基础的分析。其主要思想是通过扰动优化问题的目标函数而不是其结果来实施ε-微分隐私。作为案例研究，我们应用功能机制来解决两个最广泛使用的回归模型，即线性回归和逻辑回归。理论分析和全面的实验评估表明，该功能机制是非常有效和高效的，它显着优于现有的解决方案。
epsilon-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.