Title: Differentially Private Dataset Condensation. Abstract: Recent work in ICML'22 builds a theoretical connection between dataset condensation (DC) and differential privacy (DP) and claims that DC can provide privacy protection for free. However, the connection is problematic because of two controversial assumptions. In this paper, we revisit the ICML'22 work and elucidate the issues in the two controversial assumptions. To correctly connect DC and DP, we propose two differentially private dataset condensation (DPDC) algorithms---LDPDC and NDPDC. Through extensive evaluations on multiple datasets, we demonstrate that LDPDC has comparable performance to recent DP generative methods despite its simplicity. NDPDC provides acceptable DP guarantees with a mild utility loss, compared to the state-of-the-art DC method. Additionally, NDPDC allows a flexible trade-off between the synthetic data utility and DP budget.