Category: Allgemein
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Publication of Disseration
I am happy to announce that I finished and published my dissertation! Privacy-Preserving Analytics of Human Mobility Data – Investigating the Gap between State-Of-The-Art Privacy Methods and Real-Life Utility Requirements Abstract:In recent years, human mobility data has been increasingly collected and stored, mainly due to the ubiquitous use of smartphones, producing a constant stream of…
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Animierte Webseite zur Veranschaulichung von Re-Identifizierungsrisiken von Mobilitätsdaten
Wie leicht kann eine Person in einem vermeintlich anonymisierten Datensatz identifiziert werden? Spoiler: ziemlich leicht. Wir haben das an einem echten Datensatz durchgespielt und zeigen, wie wir einen Kollegen mit wenigen Klicks in unserem Datensatz finden konnten. Das Ergebnis ist schön visualisiert hier sehen: https://reidentifikation.freemove.space/ Außerdem zeigen wir, wie der Grad der Anonymisierung berechnet werden…
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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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Silver bullet or fool’s gold? A comprehensive survey on the utility and privacy of generative models for synthetic urban mobility data
What is synthetic data? Synthetic data is artificial data that mimics real data but the individual records are not those of actual people. It can be used to train AI models if there is not enough real data or to balance biased datasets. Lately, it is also seen as a chance to overcome privacy issues…
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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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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…
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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]…
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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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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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Attack scenarios – how adversaries retrieve sensitive information
Attack scenarios are used in privacy research to describe how an adversary potentially obtains sensitive information about a person. Such a scenario entails assumptions on side information available to an adversary, which sensitive information the adversary wants to retrieve, and how they use the side information to retrieve the sensitive information. Such scenarios are used…
Alexandra Kapp 