# Unified Locational Differential Privacy Framework

arXiv: https://arxiv.org/abs/2405.03903 · HTML: https://mlsanity.com/paper/2405.03903/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 8 references that are in the map.
- First posted on arXiv 6 May 2024.

## Builds on (most cited first, 3 of 3)

- [Diffprivlib: The IBM Differential Privacy Library](https://mlsanity.com/paper/1907.02444/) (2019)
- [Prio: Private, Robust, and Scalable Computation of Aggregate Statistics](https://mlsanity.com/paper/1703.06255/) (2017)
- [Google COVID-19 Community Mobility Reports: Anonymization Process Description (version 1.1)](https://mlsanity.com/paper/2004.04145/) (2020)

## Abstract (arXiv)

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.