# MaskMyPy: python tools for performing and analyzing geographic masks

## TL;DR

MaskMyPy is a Python tool for applying and evaluating geographic masks to protect sensitive health data while preserving spatial patterns.

## Contribution

The paper introduces MaskMyPy, a Python package that provides tools for performing, evaluating, and developing geographic masks.

## Key findings

- MaskMyPy includes functions for various geographic masks like donut, street, and Voronoi masks.
- The package includes tools to evaluate privacy and information loss in masked data.
- A case study showed that mask performance can vary significantly due to randomization.

## Abstract

Geographic masking is an important but under-utilized technique for protecting and disseminating sensitive geospatial health data. Geographic masks work by displacing static point locations such that the people those locations describe cannot be identified, while at the same time preserving important spatial patterns for analysis. Unfortunately, there is a lack of available tooling surrounding geographic masks which we believe creates an unnecessary barrier towards the adoption of these techniques. As such, this article presents a set of tools for performing, evaluating, and developing geographic masks, called MaskMyPy.

MaskMyPy is an open-source Python package that includes functions for performing geographic masks, including donut, street, location swapping, and Voronoi masks. It also includes a range of tools for evaluating the results of these masks, both with regard to privacy and information loss. Finally, it includes a special class called the ‘Atlas’ that aims to dramatically streamline mask execution and evaluation. We conducted a short case study to illustrate the power of MaskMyPy in geographic masking research, and in doing so showed that mask performance can range widely due solely to randomization. As such, we recommend that masking researchers test their masks repeatedly across a variety of test datasets.

MaskMyPy makes it easy to apply a variety of geographic masks to a set of sensitive points and then measure which mask provided the most privacy while suffering the least information loss. We believe this style of tooling is important to not only make geographic masks accessible to non-experts, but to enable expert users to better interrogate the masks they develop, and in doing so drive the geographic masking discipline forward.