Category: Allgemein
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How is mobility data sensitive information?
Political opinions, religious beliefs, sexual orientation, or health issues are typical examples of obvious sensitive information in the need of data protection. While human mobility data is also personal data, it is inherently complex and thus there are different aspects that can be considered sensitive. Is only the information about a person’s home and their…
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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…
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What is Human Mobility Data good for? Part 2 – Routing and Traffic Management
This is the second part of the series on use cases based on human mobility data. After part 1 about urban and traffic planning, we will have a closer look at routing and traffic management in this post. Unlike the mid- to long-term planning use cases in part 1, routing and traffic management applications optimize the…
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Real-Life Privacy Breaches: Why Mobility Data needs Protection
The General Data Protection Regulation (GDPR) of 2016 was a novel and unique law to secure the privacy and security of personal data, but it was not introduced without criticism. Companies see the GDPR as an obstacle that hinders them to pursue their use cases and which poses unrealistically high demands. Privacy threats on the…
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What is Human Mobility Data good for? Part 1 – Urban and Traffic Planning
There is a common misconception by data scientists who are not familiar with privacy measures that anonymization of data happens prior to and is independent of further analyses. The anonymized data can then supposedly be used for any desired use case. But the opposite is true: the better you know your use cases the more…
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Quantifying the Similarity between Maps
Using the earth mover’s distance to quantify the similarity between two maps with implementations in Python and R. When you implement a privacy measure, e.g., adding noise to data, you are interested to know how similar your noisy data is compared to your original data. Therefore, there are various similarity metrics to quantify the difference…
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Hello world!
A blog to accompany the work on my dissertation on human mobility data and privacy. This week I registered my Ph.D. proposal with the working title “Privacy-preserving Analytics of Human Mobility Data – Minimizing the utility-privacy trade-off for urban mobility applications in practice” at the Technische Universität Berlin (TU Berlin). This is what I am…
Alexandra Kapp 