Differential Privacy (DP) is a framework designed to ensure individual data remains private when conducting statistical analyses. It achieves this by introducing controlled random noise into data queries, obscuring the impact of any individual record. This process protects against re-identification and other privacy attacks, even when working with sensitive data. Differential Privacy is becoming increasingly essential as traditional privacy-preserving methods are inadequate against advanced data analysis techniques. Its application ranges from government data releases, like the US Census, to private sector data analytics. The principle behind DP is to provide useful insights from large datasets while guaranteeing that the inclusion or exclusion of any single data point doesn't significantly alter the outcome, thereby maintaining the privacy of individual data points within the dataset.