Results
Paper “Linking location-based privacy, digital sovereignty and location-based services: a meta-analysis” published
In order to shed light onto how location-based services (LBS), location privacy, and digital sovereignty are linked, the paper “Linking location privacy, digital sovereignty and location-based services: a meta review” presents a meta-review of survey papers on location privacy. We derive a set of key concepts, summarise the current state of the art in location privacy research, and identify several research gaps in the field. We also found that there is a distinct lack of research regarding digital sovereignty with respect to the management of location information. In order to help address this issue, we derive a conceptual model that links key aspects of location privacy and location-based services that we identified in the meta-review to digital sovereignty. The model provides a systematic description of the essential interactions between these concepts. This can inform the evaluation and design of future LBS in research and practice.
Workshop for learners to raise awareness of the use of personal location data
In order to be able to address the issue of data protection of position data in schools, a workshop on the use of the SIMPORT learning app was designed as part of the SIMPORT project. This workshop can be carried out in schools as well as in non-school educational settings with learners from the age of 14. The workshop is divided into two parts, each lasting 2 hours:
Teil 1: Big Data – Who fishes and who swims in the sea of location-based data?
Teil 2: “Do you want to allow location access?” – The question of the power of spatial data
The materials, which can be accessed via the download button, contain all the necessary information for teachers and learners. In addition, at least half of the participants need their own mobile device. The workshop room must be equipped with a beamer.
Midterm Report is published
At the halfway point of our project funded by the Federal Ministry of Education and Research (BMBF) in the area of “Human-Technology Interaction for Digital Sovereignty”, we have produced a report summarising the main findings and results of the SIMPORT project during its first two years.
Key outcomes of SIMPORT during its initial two years include:
- a thorough analysis of related work on location privacy;
- a detailed analysis of common apps regarding how users can configure them;
- a first version of the learning app that enables users to easily see what location data is collected about them;
- an implementation of inference/attack strategies that can be included into the learning app to convey the implications of locations sharing to app users;
- a generic architecture to provide fine-grained control to users over what location information is being shared, and a prototypical implementation of it.
Further contributions include workshops with developers and users, initial insights into how to integrate ethical considerations into the development process of LBS as well as several open-source software releases.
How is the privacy of location data and user consent handled in popular apps?
With SIMPORT, we want to enable users to manage their own location information on their mobile devices. In order to find out where to start, it is first necessary to determine what the status quo looks like for mobile applications. Specifically, the questions arise as to how current apps deal with the data protection of location data and the consent of users to use it.
In a small study, popular apps were analysed with regard to their handling of location-based data in order to answer these questions. For this purpose, various mobile apps were divided into different categories and examined with regard to predefined parameters. For example, it was examined how apps obtain consent for the use of location data and to what extent users are already informed about the handling of their data. At the same time, it is relevant what happens when users do not give this consent. Furthermore, it was analysed how accessible and detailed such information as well as settings regarding position data actually are within the apps. Within the scope of the analysis, these and numerous other parameters in the area of UI and UX, as well as the possibilities that apps give users, were specifically examined. This creates a clear picture of what users are currently confronted with and where SIMPORT can potentially come in.
SIMPORT Lern-App
Even with a few seemingly anonymous positional data, private information such as place of residence or place of work can be derived. App and operating system providers such as Google can also automatically intersect a location history with databases in order to derive, for example, shops and people visited. This is usually used to create advertisements that are individually tailored to each person and represents a deep incursion into personal and collective privacy.
In order to make users aware of the use of personal location information and the associated privacy risks, we are currently developing an app with which you can collect, visualise and evaluate your own location data in a secure environment. The app is only supposed to determine information such as the place of residence and place of work from the existing position data. One goal of the app is to be able to understand how much positional data is needed to make such personal statements. For this purpose, users can limit the analysed period, the accuracy or the level of detail of the location history to be used for the analysis. In the future, the app will be extended with further functionality, such as background information on risks and a metric for displaying the identification risk.
To ensure data protection, the app is run exclusively on the smartphone and does not exchange any data with a server: The collected position data is stored in a database on the smartphone and analysed only there. However, if no position data is to be collected, sample data can be visualised and analysed as an alternative.
The aforementioned app is currently still under development. Technically, the app is based on web technologies (HTML, JavaScript, CSS). It is developed with the Ionic Framework and uses native components from Cordova and Capacitor.
The app is open source software; the source code can be found on GitHub.