Differential Privacy is a mathematical framework designed to enable organizations to analyze datasets for patterns without compromising individual privacy. This approach addresses the limitations of traditional anonymization techniques, which often fail to prevent data re-identification. By introducing controlled randomness or "noise," Differential Privacy ensures that the output of data analysis remains consistent whether or not any individual's data is included, thus providing quantifiable privacy guarantees. There are two main deployment types: Central Differential Privacy (CDP), where a trusted curator applies privacy mechanisms, and Local Differential Privacy (LDP), where individuals modify their own data. While LDP is more secure due to decentralized data handling, it is less efficient compared to CDP. Noteworthy applications include Apple's use of Differential Privacy in iOS and macOS, and Google's implementation of privacy-preserving tools in Chrome. However, Differential Privacy is not a cure-all; it is more effective with large datasets and may require integration with technologies like full homomorphic encryption to maintain privacy in collaborative environments. Despite its advantages, such as compliance with privacy regulations and resistance to adversarial attacks, Differential Privacy has limitations, particularly in small datasets and repeated data queries, which can lead to privacy leaks.