Tag: differential privacy
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Publication – Reconsidering Utility: Unveiling the Limitations of Synthetic Mobility Data Generation Algorithms in Real-Life Scenarios
We investigated the utility of five models that create synthetic urban mobility data from raw privacy-sensitive data. Tl;dr: synthetic trips do not provide the expected high flexibility and utility and should be used with care. https://dl.acm.org/doi/10.1145/3589132.3625661 Why synthetic data? Human movement data is highly sensitive, however, data sharing is desirable for many use cases, including…
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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,…
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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]…
Alexandra Kapp 