Differential Privacy (DP) is crucial in preventing data memorisation, especially in complex models like those used in deep learning. It works by adding noise to the data or the learning process, which ensures that the model does not memorise specific, potentially sensitive data points. This is particularly important because deep learning models, due to their capacity and complexity, can inadvertently memorise and expose details from their training data. For example, if a model is trained on sensitive texts like personal emails, there's a risk it might reproduce these texts in its outputs. DP mitigates this risk by ensuring that the model's output is not overly influenced by any single training example. Thus, it maintains the confidentiality of individual data points, preventing them from being revealed directly or indirectly through the model's predictions.