Differential Privacy is crucial in deep learning and data analysis due to its ability to prevent the memorisation and exposure of sensitive training data. Deep learning models, particularly those used in language processing like Google Translate, have shown tendencies to inadvertently memorise and regurgitate training data. This poses a risk when models are trained on sensitive information such as personal emails or texts. Differential Privacy counters this by injecting noise into the data or learning algorithm, ensuring the model's output is less influenced by individual data points and reducing the risk of sensitive data exposure. This is essential for maintaining confidentiality and privacy in data-driven technologies, especially where large-scale data analysis and machine learning models are employed.