Team

CORE A/A* ranked venues marked in bold.

2021

Hannah Keller, Helen Möllering, Thomas Schneider, and Hossein Yalame. Balancing quality and efficiency in private clustering with affinity propagation (Extended Abstract). 4. Privacy Preserving Machine Learning Workshop (PPML@CCS'21), Virtual Event, November 19, 2021. Virtual Poster. To appear. [ web ]

Aditya Hegde, Helen Möllering, Thomas Schneider, and Hossein Yalame. SoK: privacy-preserving clustering (Extended Abstract). 4. Privacy Preserving Machine Learning Workshop (PPML@CCS'21), Virtual Event, November 19, 2021. Virtual Poster. To appear. [ web ]

Timm Birka, Tobias Kussel, Helen Möllering, and Thomas Schneider. An efficient and practical privacy-preserving kidney exchange problem protocol. In 33. Kryptotag (crypto day matters), Gesellschaft für Informatik e.V. / FG KRYPTO, Virtual Event, September 17, 2021. To appear. [ pdf ]

Sébastien Andreina, Giorgia Azzurra Marson, Helen Möllering, and Ghassan Karame. BaFFLe: Backdoor detection via feedback-based federated learning. In 41. IEEE International Conference on Distributed Computing Systems (ICDCS'21), IEEE, Virtual Event, July 7-10, 2021. CORE rank A. [ pdf | web ]

Hannah Keller, Helen Möllering, Thomas Schneider, and Hossein Yalame. Balancing quality and efficiency in private clustering with affinity propagation. In 18. International Conference on Security and Cryptography (SECRYPT'21), pages 173–184, SciTePress, Virtual Event, July 6-8, 2021. Full version: https://ia.cr/2021/825. Code: https://encrypto.de/code/ppAffinityPropagation. Acceptance rate 18.4%. CORE rank B. [ DOI | pdf | web ]

Aditya Hegde, Helen Möllering, Thomas Schneider, and Hossein Yalame. SoK: Efficient privacy-preserving clustering. Proceedings on Privacy Enhancing Technologies (PoPETs), 2021(4):225–248, July 2021. Online: https://ia.cr/2021/809. Code: https://encrypto.de/code/SoK_ppClustering. Acceptance rate 19.5%. CORE rank A. [ DOI | pdf | web ]

Beyza Bozdemir, Sébastien Canard, Orhan Ermis, Helen Möllering, Melek Önen, and Thomas Schneider. Privacy-preserving density-based clustering. In 16. ACM ASIA Conference on Computer and Communications Security (ASIACCS'21), pages 658–671, ACM, Virtual Event, June 7-11, 2021. Online: https://ia.cr/2021/612. Code: https://encrypto.de/code/ppDBSCAN. Acceptance rate 18.9%. CORE rank A. [ DOI | pdf | web ]

Hossein Fereidooni, Samuel Marchal, Markus Miettinen, Azalia Mirhoseini, Helen Möllering, Thien Duc Nguyen, Phillip Rieger, Ahmad-Reza Sadeghi, Thomas Schneider, Hossein Yalame, and Shaza Zeitouni. SAFELearn: Secure aggregation for private federated learning. In 4. Deep Learning and Security Workshop (DLS'21), pages 56–62, IEEE, Virtual Event, May 27, 2021. Full version: https://ia.cr/2021/386. Acceptance rate 40%. [ DOI | pdf | web ]

Hannah Keller, Helen Möllering, Thomas Schneider, and Hossein Yalame. Privacy-preserving clustering. In 32. Kryptotag (crypto day matters), Gesellschaft für Informatik e.V. / FG KRYPTO, Virtual Event, January 15, 2021. [ DOI | pdf ]

Thien Duc Nguyen, Phillip Rieger, Hossein Yalame, Helen Möllering, Hossein Fereidooni, Samuel Marchal, Markus Miettinen, Azalia Mirhoseini, Ahmad-Reza Sadeghi, Thomas Schneider, and Shaza Zeitouni. FLGUARD: Secure and private federated learning, Jan 6, 2021. https://ia.cr/2021/025.

2020

Daniel Günther, Marco Holz, Benjamin Judkewitz, Helen Möllering, Benny Pinkas, and Thomas Schneider. PEM: Privacy-preserving Epidemiological Modeling. Cryptology ePrint Archive, Report 2020/1546, December 11, 2020. https://ia.cr/2020/1546.

2019

Helen Möllering. Thwarting semantic backdoor attacks in privacy preserving federated learning. Master's thesis, Eurecom, France & University of Twente, Netherlands, August 28, 2019.

2016

Helen Möllering. Vergleich der Methoden zur Applikationsentwicklung für mobile Endgeräte mit einem Fallbeispiel unter iOS. Bachelor's thesis, Westfälische Wilhelms-Universität Münster, Germany, November 19, 2016.