We analyzed the integration of differential privacy into data synthesis for survival analyses, focusing on the trade-off between privacy protection and model accuracy. The dataset of lung cancer patients from Germany was synthesized using CTAB-GAN+. For survival analyses, the CoxPH and DeepSurv models were applied. Missing values were imputed with Miss Forest or treated as a category; in case of CoxPH categorical variables were label and one-hot encoded. Our findings show that privacy budgets significantly affect accuracy, but model choice and data preprocessing also lead to improvements of up to 4.5%. With differential privacy, the CoxPH model using Miss Forest imputation and one-hot encoding achieved a concordance index of over 0.68.