DASH: Accelerating Distributed Private Machine Learning Inference with Arithmetic Garbled Circuits. - Jonas Sander; Sebastian Berndt 001; Ida Bruhns; Thomas Eisenbarth 001 (CoRR, 2023)

Slalom at the Carnival: Privacy-preserving Inference with Masks from Public Knowledge. - Ida Bruhns; Sebastian Berndt 001; Jonas Sander; Thomas Eisenbarth 001 (IACR Commun. Cryptol., 2024)

OCEAN: Open-World Contrastive Authorship Identification. - Felix Mächtle; Jan-Niclas Serr; Nils Loose; Jonas Sander; Thomas Eisenbarth 001 (ACNS (2), 2025)

Evaluating the Impact of Face Anonymization Methods on Computer Vision Tasks: A Trade-Off Between Privacy and Utility. - Roland Stenger; Steffen Busse; Jonas Sander; Thomas Eisenbarth 001; Sebastian J. F. Fudickar (IEEE Access, 2025)

Dash: Accelerating Distributed Private Convolutional Neural Network Inference with Arithmetic Garbled Circuits. - Jonas Sander; Sebastian Berndt 001; Ida Bruhns; Thomas Eisenbarth 001 (IACR Trans. Cryptogr. Hardw. Embed. Syst., 2025)

Silenzio: Secure Non-Interactive Outsourced MLP Training. - Jonas Sander; Thomas Eisenbarth 001 (CoRR, 2025)

ReDASH: Fast and efficient Scaling in Arithmetic Garbled Circuits for Secure Outsourced Inference. - Felix Maurer; Jonas Sander; Thomas Eisenbarth 001 (CoRR, 2025)

BarkBeetle: Stealing Decision Tree Models with Fault Injection. - Qifan Wang; Jonas Sander; Minmin Jiang; Thomas Eisenbarth 001; David F. Oswald (CoRR, 2025)