In machine learning, there's a trade-off between privacy and model performance. Implementing differential privacy typically involves adding noise to the training process, which can affect the accuracy of the model. The more privacy is enforced (lower epsilon values), the more noise is added, potentially leading to decreased model performance. This trade-off is a key consideration in applications where both high accuracy and privacy are important. Balancing these aspects requires careful tuning of the privacy parameters and possibly accepting some compromise on model performance to ensure adequate privacy protection.