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 …

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 …

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 …

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 …

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 …

Although these devices enable their users to monitor their activities and health, they also raise new security and privacy concerns, given the sensitive data (e.g., steps, heart rate) they …

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 …

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, …

Clearly, naively linking mobile phone data with health records would violate privacy by either allowing to track mobility patterns of infected individuals, leak information on who is infected, …

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 …