The notebooks provided in this repository give a brief introduction to methods for location data privacy preservation. These notebooks are intended for an audience that is familiar with python, has some familiarity with geospatial data, and little to no experience with privacy algorithms.

There are three tutorials in this repository. Each has both a Jupyter Notebook version and raw python code version.

This tutorial presents an introduction to geomasking, an obfuscation technique used for preserving the privacy of geographic data. Through this tutorial we will explore two forms of geomasking, one using point geometries and the other using buffers.

JupyterNotebook | Python Code

This tutorial presents an introduction to (spatial) k-anonymity, a data anonymization technique that is used to protect individuals' privacy in a dataset. Through this tutorial we will explore both non-spatial and spatial k-anonymity.

JupyterNotebook | Python Code

This tutorial presents an introduction to differential privacy, a mathematical framework for ensuring the privacy of individuals or records in datasets. Through this tutorial we will explore differential privacy applied to our micromobility trip dataset.

JupyterNotebook | Python Code

Author: Grant McKenzie [[email protected] | https://grantmckenzie.com]