Truthful Linear Regression
Truthful Linear Regression
复制标题
真实的线性回归
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Katrina Ligett
中科院分区：
文献类型：
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作者：
Rachel Cummings;Stratis Ioannidis;Katrina Ligett
我们考虑将线性模型拟合到关注其隐私的个人持有的数据的问题。激励大多数玩家将其数据如实向分析师报告，这将我们的设计限制为为参与者提供隐私保证的机制；我们使用差异隐私来模拟个人的隐私损失。这立即提出了一个问题，因为线性模型的差异私有计算必然会产生偏见的估计，并且现有的设计机制的方法可以从隐私敏感的个体中获取数据并不能很好地概括为有偏见的估计器。我们通过适当的计算和付款方案设计克服了这一挑战。
We consider the problem of fitting a linear model to data held by individuals who are concerned about their privacy. Incentivizing most players to truthfully report their data to the analyst constrains our design to mechanisms that provide a privacy guarantee to the participants; we use differential privacy to model individuals' privacy losses. This immediately poses a problem, as differentially private computation of a linear model necessarily produces a biased estimation, and existing approaches to design mechanisms to elicit data from privacy-sensitive individuals do not generalize well to biased estimators. We overcome this challenge through an appropriate design of the computation and payment scheme.