In this work, we study high-dimensional mean estimation under user-level
differential privacy, and design an $(\varepsilon,\delta)$-differentially
private mechanism using as few users as possible. In particular, we provide a
nearly optimal trade-off between the number of users and the number of samples
per user required for private mean estimation, even when