The rapid iteration of large-scale deep learning models leads to an ever-growing demand for high-quality training data. This demand is typically met by collecting massive amounts of data from the Internet, which often include private information such as personal images and text, thus creating risks related to privacy leakage and ethical concerns. To mitigate such issues, researchers have proposed unlearnable examples as a proactive strategy for data privacy protection. This approach embeds imperceptible perturbations into training data, making unauthorized models appear to achieve high training accuracy while actually losing generalization ability due to learning meaningless “shortcut features.” This study provides a systematic review of recent advances in unlearnable examples, categorizing existing work into two main areas: generation methods and purification and detection mechanisms. Building on this foundation, future research challenges and potential directions are further explored, with the aim of providing theoretical support and facilitating technical breakthroughs in private data protection.