Unified Locational Differential Privacy Framework
This work addresses privacy concerns in geographical data analysis for applications like income or disease spread, but it is incremental as it unifies existing local DP mechanisms.
The paper tackled the problem of aggregating sensitive geographical data by introducing a unified locational differential privacy framework, which provides formal privacy guarantees for various data types and demonstrates utility on four datasets.
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.