Welcome to VisualAnon!

VisualAnon is an app generated by researchers at the Friedrich-Alexander-University Erlangen-Nürnberg in collaboration with the University of Birmingham. Following our research about the anonymity of fitness apps that showed at the example of Germany how considerably the anonymity decreases by the leakage of socio-democratic data, we now created VisualAnon to cause awareness about the anonymity set people live in, not only for Germany but for arbitrary countries.

Anonymity sets and their importance

People provide or donate data on several occasions in life: at the registration for an App, during an online survey or at the grocery store, where people are sometimes asked about the zip code. The general statement is that the collected data is private because personal identifiable information is not collected. However, how anonymous are we providing which data?

With VisualAnon we aim to create awareness of the sensitivity of your personal data by visualizing your anonymity set. Your anonymity set is the number of people who share the same characteristics. For example, if you only donate your zip code, then the anonymity set consists of all people living in this area code. The more data you provide, the smaller the set. If you also provide your gender, then the set drecreases by 50%. With VisualAnon we want to show you how your anonymity decreases when you share personal data that might seem irrelevant.

Our research based on data for Germany showed that the level of anonymity is really small even if the data is pseudonymous. But we were able to solve this problem by using so called Differential privacy.

If you donate your data to us, this information will be aggregated directly.

Check my anonymity set size

Research

  • Sweeny, L. Simple demographics often identify people uniquely. Health (San Francisco) 671, 2000 (2000), 1–34.
  • Rocher, Hendrickx & Montjoye Estimating the success of re-identifications in incomplete datasets using generative models. Nature Communications, 2019
  • Berrang and Schröder, Pandemic Privacy, 2021