Process mining techniques enable organizations to analyze business process execution traces in order to identify opportunities for improving their operational performance. Oftentimes, such execution traces contain private information. For example, the execution traces of a healthcare process are likely to be privacy-sensitive. In such cases, organizations need to deploy Privacy-Enhancing Technologies (PETs) to strike a balance between the benefits they get from analyzing these data and the requirements imposed onto them by privacy regulations, particularly that of minimizing re-identification risks when data are disclosed to a process analyst. Among many available PETs, differential privacy stands out for its ability to prevent predicate singling out attacks and its composable privacy guarantees. A drawback of differential privacy is the lack of interpretability of the main privacy parameter it relies upon, namely epsilon. This leads to the recurrent question of how much epsilon is enough? This article proposes a method to determine the epsilon value to be used when disclosing the output of a process mining technique in terms of two business-relevant metrics, namely absolute percentage error metrics capturing the loss of accuracy (a.k.a. utility loss) resulting from adding noise to the disclosed data, and guessing advantage, which captures the increase in the probability that an adversary may guess information about an individual as a result of a disclosure. The article specifically studies the problem of protecting the disclosure of the so-called Directly-Follows Graph (DFGs), which is a process mining artifact produced by most process mining tools. The article reports on an empirical evaluation of the utility-risk trade-offs that the proposed approach achieves on a collection of 13 real-life event logs.