DP Mobility Report: A Python package for quick explorations and mobility data reports with privacy guarantees
Exploratory data analysis is an essential step in any data science project, as it allows us to understand the data and identify patterns, trends, and anomalies. However, exploratory analyses can often be time-consuming and repetitive. While there are existing packages for performing exploratory analyses on tabular data, e.g., ydata_profiling (formerly known as pandas_profiling) for Python,…
You’re more unique than you think – about the difficulty of anonymizing mobility data
One of the main reasons why people can easily be re-identified in mobility data is because mobility patterns are highly unique. Consider your visited locations over the last few days, where did you go and when? E.g., you have been to your home, university, fitness studio, and your favorite supermarket. This combination of locations visited…
Publication: Towards mobility reports with user-level privacy
Mobility data, even aggregated statistics, can usually not be shared without privacy concerns. Within this publication, my co-authors Saskia Nuñez von Voigt, Helena Mihaljević, and Florian Tschorsch and I aim to provide a report that compiles typical analyses of urban human mobility and provides privacy guarantees so that it can be shared freely. [Download paper]…
Publication: Collection, usage and privacy of mobility data in the enterprise and public administrations
What mobility data sources are used in practice? For which purposes? Which models are used? What privacy enhancements are already implemented? I asked 13 experts from public administrations, public transit companies, mobility platforms and apps, automobile manufacturers, sensor companies, and market research companies and presented my results at the Privacy Enhancing Technologies Symposium 2022, which…
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Alexandra Kapp 