16 citations · 21 across the 6 of their papers we have counts for
6 papers
Learning from Anonymized and Incomplete Tabular Data
Lucas Lange, Adrian Böttinger, Victor Christen +3
User-driven privacy allows individuals to control whether and at what granularity their data is shared, leading to datasets that mix original, generalized, and missing values withi…
Federated Learning With Individualized Privacy Through Client Sampling
Lucas Lange, Ole Borchardt, Erhard Rahm
With growing concerns about user data collection, individualized privacy has emerged as a promising solution to balance protection and utility by accounting for diverse user privac…
Assessing the Impact of Image Dataset Features on Privacy-Preserving Machine Learning
Lucas Lange, Maurice-Maximilian Heykeroth, Erhard Rahm
Machine Learning (ML) is crucial in many sectors, including computer vision. However, ML models trained on sensitive data face security challenges, as they can be attacked and leak…
Generating Synthetic Health Sensor Data for Privacy-Preserving Wearable Stress Detection
Lucas Lange, Nils Wenzlitschke, Erhard Rahm
Smartwatch health sensor data are increasingly utilized in smart health applications and patient monitoring, including stress detection. However, such medical data often comprise s…
Slice it up: Unmasking User Identities in Smartwatch Health Data
Lucas Lange, Tobias Schreieder, Victor Christen +1
Wearables are widely used for health data collection due to their availability and advanced sensors, enabling smart health applications like stress detection. However, the sensitiv…
Privacy in Practice: Private COVID-19 Detection in X-Ray Images (Extended Version)
Lucas Lange, Maja Schneider, Peter Christen +1
Machine learning (ML) can help fight pandemics like COVID-19 by enabling rapid screening of large volumes of images. To perform data analysis while maintaining patient privacy, we…