Existing research has explored the explanation of differential privacy in health contexts. Our study adapts well-performing textual descriptions of local differential privacy from prior work to a new …

In contrast to prior findings in the health context, adding an implications statement to LDP and other PET descriptions (FL, FL+LDP, and GT) only marginally improved understanding in our study, but also did …

René Raab (Friedrich-Alexander-Universität Erlangen-Nürnberg), Arijana Bohr (Friedrich-Alexander-Universität Erlangen-Nürnberg), Kai Klede (Friedrich-Alexander-Universität Erlangen-Nürnberg), …

Finally, we identify sensitive finance and health websites that have installed trackers that are likely configured to collect form data PII in violation of Meta and Google policies.

In this paper, we consider a common problem when analyzing health data: estimating means for different groups. We discuss a generic privacy-preserving method for approximating the means of …

In this paper, we propose SenRev to systematically measure the leakages of sensitive information in those publicly available discussions. We use SenRev to analyze 1,894,900 multi-modal and multi …

Abstract: Through voice characteristics and manner of expression, even seemingly benign voice recordings can reveal sensitive attributes about a recorded speaker (e. g., geographical origin, health …

Such similar patient queries (SPQs) are of utmost importance to medical practice and will be relied upon in future health information exchange systems. While privacy-preserving solutions have been …

We hope that our work will educate the currently ongoing design of vaccination certificates, give us deeper insights into the privacy of health-related data and apps, and prepare us for future potential …

Abstract: Recent studies have shown that machine learning can identify individuals with mental illnesses by analyzing their social media posts. Topics and words related to mental health are some of the top …