Differentially Private Confidence Intervals for Empirical Risk Minimization

11 Apr 2018 Yue Wang Daniel Kifer Jaewoo Lee

The process of data mining with differential privacy produces results that are affected by two types of noise: sampling noise due to data collection and privacy noise that is designed to prevent the reconstruction of sensitive information. In this paper, we consider the problem of designing confidence intervals for the parameters of a variety of differentially private machine learning models... (read more)

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