Integrating differential privacy into machine learning can impact model accuracy. The noise added to ensure privacy can affect the precision of the learned model, especially in complex models like deep neural networks. The challenge lies in adding enough noise to preserve privacy while maintaining the fidelity of the model's predictions. This balance is critical in scenarios where accurate predictions are crucial, such as healthcare or financial forecasting. Research in this area focuses on developing algorithms that can learn effectively even with the constraints imposed by differential privacy.