The recent advancements in the field of privacy-preserving technologies for data-driven applications, particularly in high-stakes domains such as healthcare and autonomous vehicles, have seen a significant shift towards leveraging synthetic data generation and differential privacy mechanisms. Researchers are increasingly focusing on developing methods that not only protect sensitive information but also maintain the utility and fidelity of the data for downstream tasks. The integration of Variational Autoencoders (VAEs) and knowledge distillation techniques has shown promise in enhancing the efficiency and accuracy of intrusion detection systems in autonomous vehicles, while also ensuring transparency through Explainable AI (XAI) methods. Additionally, the use of data-adaptive differentially private algorithms for in-context learning in large language models (LLMs) has been explored to balance privacy and performance. However, recent studies have also highlighted vulnerabilities in current differential privacy implementations, particularly in the context of text sanitization, where large language models can potentially reconstruct private information. This underscores the need for continuous innovation and rigorous evaluation to ensure robust privacy protection in data-driven technologies.