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Talk Presented at NIST Workshop on Privacy-Enhancing Cryptography 2024
2024/10/18
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Paper and two posters accepted at ACSAC’24
2024/09/24
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Research Demonstrated to Hessian Parliament Members Lucas Schmitz and Peter Franz
2024/09/16
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Disputation of Hossein Yalame
2024/09/11
On Tuesday, September 10, 2024 Hossein Yalame successfully defended his dissertation “Advancing MPC: From Real-Works Applications to LUT-Based Protocols”
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Three Works accepted at CCS'24 and Affiliated Workshops
2024/08/21
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ENCRYPTO Group organized TPMPC'24 in Darmstadt
2024/06/17
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Four papers at IEEE S&P'24
2024/05/20
The following four papers by members of ENCRYPTO group will be presented at the top conference 45. IEEE Symposium on Security and Privacy 2024 (IEEE S&P'24): Andreas Brüggemann, Oliver Schick, Thomas Schneider, Ajith Suresh, Hossein Yalame. Don't Eject the Impostor: Fast Three-party Computation with a Known Cheater. In IEEE S&P'24. Qi Pang, Jinhao Zhu, Helen Möllering, Wenting Zheng, Thomas Schneider. BOLT: Privacy-preserving, Accurate and Efficient Inference for Transformers. In IEEE S&P'24. Kasra Edalatnejad, Wouter Lueks, Justinas Sukaitis, Vincent Graf Narbel, Massimo Marelli, Carmela Troncoso. Janus: Safe Biometric Deduplication for Humanitarian Aid Distribution. Banashri Karmakar, Nishat Koti, Arpita Patra, Sikhar Patranabis, Protik Paul, Divya Ravi. Asterisk: Super-fast MPC with a Friend.
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Paper accepted at ASIACCS'24
2024/05/03
Menhir: An Oblivious Database with Protection against Access and Volume Pattern Leakage by Leonie Reichert, Gowri R Chandran, Phillipp Schoppmann, Thomas Schneider and Björn Scheuermann was accepted at the top conference 19th ACM ASIA Conference on Computer and Communications Security (ASIACCS'24). This project is a collaboration with Leonie Reichert and Björn Scheuermann from the KOM group at TU Darmstadtand Phillipp Schoppmann from Google. It uses differential privacy techniques to prevent access and volume pattern leakage in databases.
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Runner-Up Distinguished Paper Award for SaTML'24 Paper
2024/04/15
Runner-Up Distinguished Paper Award for SaTML'24 Paper
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Paper accepted at ESORICS'24
2024/04/09
Attesting Distributional Properties of Training Data for Machine Learning by Vasisht Duddu, Anudeep Das, Nora Khayata, Hossein Yalame, Thomas Schneider, and N. Asokan was accepted at the top conference 29th European Symposium on Research in Computer Security (ESORICS) 2024. This paper is a collaboration with Vasisht Duddu, Anudeep Das, and N. Asokan from the University of Waterloo. It proposes the notion of property attestation using Machine Learning, cryptographic techniques, and a mix of both to demonstrate relevant distributional properties of training data in conjunction with the trained model without revealing the data.