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Number of items: 4.

Boemer, Fabian and Cammarota, Rosario and Demmler, Daniel and Schneider, Thomas and Yalame, Mohammad Hossein (2020):
MP2ML: A Mixed-Protocol Machine Learning Framework for Private Inference (Extended Abstract).
ACM, Privacy Preserving Machine Learning in Practice (PPMLP'20) – CCS 2020 Workshop, Orlando, USA, 09.-13.11., [Conference or Workshop Item]

Boemer, Fabian and Cammarota, Rosario and Demmler, Daniel and Schneider, Thomas and Yalame, Mohammad Hossein (2020):
MP2ML: A mixed-protocol machine learning framework for private inference.
15th International Conference on Availability, Reliability and Security (ARES'20), virtual Conference, 25.-28.08, [Conference or Workshop Item]

Boemer, Fabian and Cammarota, Rosario and Demmler, Daniel and Schneider, Thomas and Yalame, Mohammad Hossein (2020):
MP2ML: A Mixed-Protocol Machine Learning Framework for Private Inference (Contributed Talk).
The 2nd Privacy-Preserving Machine Learning Workshop, virtual Conference, 16.08.2020, [Conference or Workshop Item]

Cammarota, Rosario and Schunter, Matthias and Rajan, Anand and Boemer, Fabian and Kiss, Ágnes and Treiber, Amos and Weinert, Christian and Schneider, Thomas and Stapf, Emmanuel and Sadeghi, Ahmad-Reza and Demmler, Daniel and Chen, Huili and Hussain, Siam Umar and Riazi, M. Sadegh and Koushanfar, Farinaz and Gupta, Saransh and Rosing, Simunic (2020):
Trustworthy AI Inference Systems: An Industry Research View.
In: arXiv/Computer Science/Cryptography and Security, Version 1, [Report]

This list was generated on Tue Apr 20 00:47:12 2021 CEST.