Towards Practical Differential Privacy in Data Analysis: Understanding the Effect of Epsilon on Utility in Private ERM. (arXiv:2206.03488v1 [cs.CR])

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 …

analysis data data analysis differential privacy privacy