One of the main challenges in differential privacy is balancing the trade-off between privacy protection and the utility of the data. Adding noise to protect privacy can reduce the accuracy or usability of the data. The challenge lies in determining the right amount of noise to add: too little noise might compromise privacy, while too much noise can render the data useless. This balance is often managed through the privacy parameter epsilon, where a smaller epsilon provides stronger privacy at the cost of data utility. Finding the optimal balance requires careful consideration of the specific data analysis goals and privacy requirements.