  Several companies (e.g., Meta, Google) have initiated "data-for-good"
projects where aggregate location data are first sanitized and released
publicly, which is useful to many applications in transportation, public health
(e.g., COVID-19 spread) and urban planning. Differential privacy (DP) is the
protection model of choice to ensure the privacy of the individuals who
generated the raw location data. However, current solutions fail to preserve
data utility when each individual contributes multiple location reports (i.e.,
under user-level privacy). To offset this limitation, public releases by Meta
and Google use high privacy budgets (e.g., $\epsilon$=10-100), resulting in
poor privacy. We propose a novel approach to release spatio-temporal data
privately and accurately. We employ the pattern recognition power of neural
networks, specifically variational auto-encoders (VAE), to reduce the noise
introduced by DP mechanisms such that accuracy is increased, while the privacy
requirement is still satisfied. Our extensive experimental evaluation on real
datasets shows the clear superiority of our approach compared to benchmarks.
