In this chapter, we focus on data privacy in machine learning and explore the robustness of quantum machine learning models against data privacy breaches. The primary motivation is to identify a novel form of quantum advantage by discovering quantum machine learning models that offer enhanced robustness against privacy breaches compared to classical machine learning models. While this line of research is important in its own right, it also serves as an example for demonstrating how Lie-algebraic techniques can be employed in practice to characterize the privacy of quantum machine learning models.