  Aggregating statistics over geographical regions is important for many
applications, such as analyzing income, election results, and disease spread.
However, the sensitive nature of this data necessitates strong privacy
protections to safeguard individuals. In this work, we present a unified
locational differential privacy (DP) framework to enable private aggregation of
various data types, including one-hot encoded, boolean, float, and integer
arrays, over geographical regions. Our framework employs local DP mechanisms
such as randomized response, the exponential mechanism, and the Gaussian
mechanism. We evaluate our approach on four datasets representing significant
location data aggregation scenarios. Results demonstrate the utility of our
framework in providing formal DP guarantees while enabling geographical data
analysis.
