Tag: privacy-enhancing techniques
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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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How much privacy do “Privacy Zones” provide?
In this post, I gave an overview of different privacy-enhancing techniques for mobility data. This post is the first to deep-dive into single specific techniques by looking into privacy zones. Social-network sports apps like Strava are used to track and share personal sporting performances, including pictures and GPS traces. This has led to undesired events:…
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Privacy-preserving techniques and how they apply to mobility data
What does it actually mean to “anonymize” data? With this blog post, I want to give an overview of different established methods and how they apply to mobility data. (The survey of Fiore et al., 2020 has been a major source for this overview). First of all, it should be noted, that there is a…
Alexandra Kapp 