Helen Möllering

Helen Möllering is a researcher at the Technical University of Darmstadt, Germany. She holds a PhD in Computer Science from the same institution, awarded in 2023 for her thesis titled "Towards Practical Privacy-Preserving Clustering and Health Care Data Analyses". Her research focuses on privacy-preserving techniques in machine learning, cryptography, and healthcare data analysis. Key areas include federated learning, secure multiparty computation, and applications in medical data privacy.

• PhD: Technical University of Darmstadt (2023)
• Primary Affiliation: Technical University of Darmstadt

Her work emphasizes balancing privacy and utility in data-driven systems, particularly in healthcare and epidemiological modeling. Notable collaborations include projects with Thomas Schneider, Benny Pinkas, and Hossein Yalame on secure aggregation protocols and privacy-preserving clustering algorithms.

Recent publications highlight advancements in federated learning security, such as "ScionFL" and "BOLT", which address robustness and efficiency in distributed machine learning systems. She also contributes to applications like secure kidney exchange problem solutions and epidemiological modeling under privacy constraints.

Co-author networks include prominent researchers in cryptography and data security, reflecting her interdisciplinary approach to tackling privacy challenges in computational systems.