As datasets and models grow in size and complexity to increase performance, the risks associated with sensitive data also grow. Differential privacy (DP) offers a framework for designing mechanisms that provide a degree of privacy that can help conceal sensitive features or information. However, different domains and applications can naturally exhibit different rates of trade-offs between privacy and performance depending on their characteristics. In contrast to well-studied areas (e.g., healthcare), one relatively unexplored domain is network traffic analysis where the data contains sensitive information on users' communications. In this paper, we apply DP to various machine learning models trained to classify between encrypted and non-encrypted packets from network traffic; we emphasize that our goal is to examine a relatively unexplored area to analyze the trade-offs between privacy and performance when the data contains both encrypted and un-encrypted observations. We show how varying model architecture and feature sets can be a relatively simple way to achieve more optimal performance-privacy trade-offs; we also compare and contextualize reasonable privacy budgets from our analysis in the network traffic domain against those in other more well-studied domains.