Large language models (LLMs) are impressive, but they have a privacy problem. They can sometimes memorize and leak sensitive information from their training data. Researchers are constantly looking for ways to fix this, and a new technique called PSY (Posterior Sampling based Privacy enhancer) offers a promising solution. Think of an LLM learning like a student studying for an exam. Sometimes, the student memorizes the material word-for-word instead of truly understanding it. This is similar to how LLMs can inadvertently memorize sensitive data. PSY works by adding a bit of “creative blur” to the LLM’s learning process. It uses a technique called posterior sampling, which is like introducing controlled randomness into what the model remembers. This makes it harder for the model to memorize specific data points while still allowing it to learn the overall patterns in the data. Researchers tested PSY on several popular LLMs and found it significantly reduced privacy leaks without hurting the model’s performance. They subjected the models to membership inference attacks (MIAs) and data extraction attacks (DEAs), which are ways hackers try to pry out sensitive information. PSY proved effective against these attacks, offering a boost to LLM privacy. While PSY shows great potential, there's still more work to do. Researchers are exploring how to combine PSY with other fine-tuning methods and looking at ways to formally measure the privacy guarantees it provides. This research direction offers a fresh approach to enhancing LLM privacy and paves the way for more secure and responsible AI development.