Unified Locational Differential Privacy Framework

TL;DR

This paper introduces a unified differential privacy framework for securely aggregating diverse types of geographical data, ensuring privacy while maintaining data utility across multiple real-world scenarios.

Contribution

It presents a comprehensive framework that applies various local DP mechanisms to different data types for geographical data aggregation, a novel unification in this domain.

Findings

Framework provides formal DP guarantees.

Effective across multiple data types and scenarios.

Maintains data utility while protecting privacy.

Abstract

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.