Differential Privacy provides a mathematically rigorous way to maximize the accuracy of queries from statistical databases while minimizing the chances of identifying its entries. Anonymity alone is not sufficient; differential privacy allows researchers to see the big picture in data analysis while ensuring that individual privacy is strictly protected. This is typically achieved by adding noise to the results of queries, making it difficult to identify if a particular individual's data has been included or excluded from the database.