论文标题
在数据分析中迈向实践差异隐私：了解Epsilon对私人ERM中效用的影响
Towards Practical Differential Privacy in Data Analysis: Understanding the Effect of Epsilon on Utility in Private ERM
论文作者
论文摘要
在本文中，我们将注意力集中在私人经验风险最小化（ERM）上，这是最常用的数据分析方法之一。我们通过从理论上探索Epsilon（决定隐私保证强度的差异隐私参数）对学习模型实用性的效果（差异隐私的参数）迈出了解决上述问题的第一步。我们通过修改Epsilon追踪实用性的变化，并揭示了Epsilon和效用之间建立的关系。然后，我们正式化了这种关系，并提出了一种实用方法来估算Epsilon的任意价值。理论分析和实验结果都表明我们在实际应用中的方法的估计准确性和广泛的适用性。由于为算法提供强大的公用事业保证，在可能的情况下也越来越接受隐私，我们的方法将具有很高的实际价值，并且可能会被希望保留隐私但不愿意妥协的公司和组织采用。
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.
