Large language models (LLMs) are revolutionizing how we interact with information, from summarizing documents to answering complex questions. But this power comes at a price: our privacy. When we feed sensitive data into these models, we risk exposing confidential information to third-party providers. How can we harness the power of LLMs while keeping our secrets safe? Researchers are exploring ways to sanitize text before submitting it to LLMs. Think of it like scrubbing your personal data before sending a message—removing names, addresses, or other identifying details. However, over-sanitizing can cripple the LLM's ability to understand and process the text, leading to useless results and wasted resources, especially when using paid LLM services. This research dives into the delicate balancing act between privacy and utility. It proposes a clever system: using a smaller, local language model (SLM) as a gatekeeper. Before sending sanitized text to a powerful online LLM, the SLM predicts how much the sanitization will impact the quality of the results. If the predicted utility is too low, the user is alerted, saving them from unnecessary costs and frustration. The research focused on text summarization as a test case. They used a variety of SLMs and a larger LLM, applying different levels of sanitization to news articles. The results showed a significant challenge: current sanitization methods, while effective for privacy, can severely degrade the quality of summaries. The larger the LLM, the more sensitive it is to these changes, highlighting the need for more nuanced approaches. While the addition of a local SLM didn't drastically improve prediction accuracy in this study, it represents a promising direction for future research. This work also uncovered a critical flaw in a common text sanitization technique called distance-based differential privacy. This technique relies on replacing words with similar ones, but the research found that the implementation often defaults to returning the original word, negating any privacy benefits. This discovery emphasizes the importance of rigorous testing and refinement of these techniques. The quest to balance privacy and utility in LLM interactions is ongoing. This research sheds light on the challenges, prompting further investigation into more sophisticated sanitization and prediction methods. As LLMs become increasingly integrated into our lives, ensuring privacy without sacrificing performance is paramount.