We apply our tool to analyze four online health communities to explore the sensitive data flow pattern and the leakage caused by diferent interactions. Our findings are alarming.

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 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 …

From tracking daily eating habits and vital functions to monitoring sleep patterns and even the menstrual cycle, these apps have become ubiquitous in their pursuit of comprehensive health …

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 …

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

While being beneficial to improve security and privacy for service providers, their solution requires strong trust assumption for the (central) validation service that learns all health-related details …

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 …

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 …

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, …