The notion of $\varepsilon$-differential privacy is a widely used concept of providing quantifiable privacy to individuals. However, it is unclear how to explain the level of privacy protection provided by a differential privacy mechanism with a set $\varepsilon$. In this study, we focus on users' comprehension of the privacy protection provided by a differential privacy mechanism. To do so, we study three variants of explaining the privacy protection provided by differential privacy: (1) the original mathematical definition; (2) $\varepsilon$ translated into a specific privacy risk; and (3) an explanation using the randomized response technique. We compare users' comprehension of privacy protection employing these explanatory models with their comprehension of privacy protection of $k$-anonymity as baseline comprehensibility. Our findings suggest that participants' comprehension of differential privacy protection is enhanced by the privacy risk model and the randomized response-based model. Moreover, our results confirm our intuition that privacy protection provided by $k$-anonymity is more comprehensible.