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.

Runner-Up Distinguished Paper Award for SaTML'24 Paper

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.

HyCaMi: High-Level Synthesis for Cache Side-Channel Mitigation by Heiko Mantel, Joachim Schmidt, Thomas Schneider, Maximilian Stillger, Tim Weißmantel, and Hossein Yalame was accepted at the top conference 61. Design Automation Conference (DAC'24). The paper is a collaboration with the MAIS group within projects E3 (Secure Refinement of Cryptographic Algorithms) and E4 (Compiler for Privacy-Preserving Protocols) of the collaborative research center CROSSING. It shows how to use high-level synthesis tools for mitigation of cache side-channels.

Chinese Authorities De-anonymize AirDrop Users

Thomas Schneider is a program committee member of ACM CCS 2024

Best Exercise at CS Department: CRYPROT SS23

Laura Hetz wins 1st Prize at Frauen-MINT-Award with her Master Thesis.

Thomas Schneider was awarded with an ERC Consolidator Grant 2023 for the project Tools for Protecting Data and Function Privacy (PRIVTOOLS).