In this paper, we focus our attention on private Empirical Risk Minimization (ERM), which is one of the most commonly used data analysis method. We take the first step towards solving the above problem by theoretically exploring the effect of epsilon (the parameter of differential privacy that determines the strength of privacy guarantee) on utility of the learning model. We trace the change of utility with modification of epsilon and reveal an established relationship between epsilon and utility. We then formalize this relationship and propose a practical approach for estimating the utility under an arbitrary value of epsilon. Both theoretical analysis and experimental results demonstrate high estimation accuracy and broad applicability of our approach in practical applications. As providing algorithms with strong utility guarantees that also give privacy when possible becomes more and more accepted, our approach would have high practical value and may be likely to be adopted by companies and organizations that would like to preserve privacy but are unwilling to compromise on utility.

通过理论探索差分隐私强度参数epsilon对学习模型效用的影响，建立并提出了一种可以在任意epsilon值下对效用进行估计的实用方法，该方法在实验结果表现出高精度估计和广泛适用性，同时能够在保障隐私的前提下提供较强的效用保证。

私有ERM中隐私保护的实用性差分隐私：理解Epsilon对实用性的影响