Differential privacy impacts data utility by introducing a trade-off between privacy protection and the accuracy or usefulness of the data. The addition of noise to protect privacy can reduce the precision of the data analysis results. Balancing this trade-off involves calibrating the amount of noise added based on the desired level of privacy. Higher privacy protection (lower epsilon values) typically means more noise, which can reduce utility. The challenge is to find an optimal balance where sufficient privacy is ensured without overly compromising the quality and usefulness of the data. This balance is crucial in applications where both data privacy and accuracy are important, such as in healthcare and finance.