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

A Neural Approach to Spatio-Temporal Data Release with User-Level Differential Privacy