CORE A/A* ranked venues marked in bold.

2020

Amos Treiber, Alejandro Molina, Christian Weinert, Thomas Schneider, and Kristian Kersting. CryptoSPN: Expanding PPML beyond neural networks (Extended Abstract). In Privacy-Preserving Machine Learning in Practice Workshop (PPMLP@CCS'20), pages 9–14, ACM, Virtual Event, November 9, 2020. [ DOI | pdf | web ]

Amos Treiber, Alejandro Molina, Christian Weinert, Thomas Schneider, and Kristian Kersting. CryptoSPN: Privacy-preserving sum-product network inference. In 24. European Conference on Artificial Intelligence (ECAI'20), pages 1946–1953, IOS Press, Virtual Event, August 29-September 5, 2020. Online: https://arxiv.org/abs/2002.00801. Code: https://encrypto.de/code/CryptoSPN. Acceptance rate 26.8%. CORE rank A. [ DOI | pdf | web ]

Amos Treiber, Alejandro Molina, Christian Weinert, Thomas Schneider, and Kristian Kersting. CryptoSPN: Expanding PPML beyond neural networks (Contributed Talk). 2. Privacy-Preserving Machine Learning Workshop (PPML@CRYPTO'20), August 16, 2020. [ web ]

Rosario Cammarota, Matthias Schunter, Anand Rajan, Fabian Boemer, Ágnes Kiss, Amos Treiber, Christian Weinert, Thomas Schneider, Emmanuel Stapf, Ahmad-Reza Sadeghi, Daniel Demmler, Huili Chen, Siam Umar Hussain, Sadegh Riazi, Farinaz Koushanfar, Saransh Gupta, Tajan Simunic Rosing, Kamalika Chaudhuri, Hamid Nejatollahi, Nikil Dutt, Mohsen Imani, Kim Laine, Anuj Dubey, Aydin Aysu, Fateme Sadat Hosseini, Chengmo Yang, Eric Wallace, and Pamela Norton. Trustworthy AI inference systems: An industry research view, August 10, 2020. https://arxiv.org/abs/2008.04449. [ arXiv ]

Amos Treiber, Alejandro Molina, Christian Weinert, Thomas Schneider, and Kristian Kersting. CryptoSPN: Privacy-preserving machine learning beyond neural networks (Contributed Talk). Theory and Practice of Multi-Party Computation Workshop (TPMPC'20), June 4, 2020. [ web ]

Thomas Schneider and Amos Treiber. A comment on privacy-preserving scalar product protocols as proposed in “SPOC”. IEEE Transactions on Parallel and Distributed Systems (TPDS), 31(3):543–546, March, 2020. Full version: https://arxiv.org/abs/1906.04862. Code: https://encrypto.de/code/SPOCattack. CORE rank A*. [ DOI | pdf | web ]

2019

Sebastian P. Bayerl, Ferdinand Brasser, Christoph Busch, Tommaso Frassetto, Patrick Jauernig, Jascha Kolberg, Andreas Nautsch, Korbinian Riedhammer, Ahmad-Reza Sadeghi, Thomas Schneider, Emmanuel Stapf, Amos Treiber, and Christian Weinert. Privacy-preserving speech processing via STPC and TEEs (Extended Abstract). 2. Privacy Preserving Machine Learning Workshop (PPML@CCS'19), London, UK, November 15, 2019. Poster. Acceptance rate 55.0%. [ pdf | poster | web ]

Andreas Nautsch, Abelino Jiménez, Amos Treiber, Jascha Kolberg, Catherine Jasserand, Els Kindt, Héctor Delgado, Massimiliano Todisco, Mohamed Amine Hmani, Aymen Mtibaa, Mohammed Ahmed Abdelraheem, Alberto Abad, Francisco Teixeira, Driss Matrouf, Marta Gomez-Barrero, Dijana Petrovska-Delacrétaz, Gérard Chollet, Nicholas Evans, Thomas Schneider, Jean-François Bonastre, Bhiksha Raj, Isabel Trancoso, and Christoph Busch. Preserving privacy in speaker and speech characterisation. Computer Speech and Language (CSL), 2019(58):441–480, November, 2019. CORE rank A. [ DOI | pdf | web ]

Amos Treiber, Andreas Nautsch, Jascha Kolberg, Thomas Schneider, and Christoph Busch. Privacy-preserving PLDA speaker verification using outsourced secure computation. Speech Communication, 114:60–71, November, 2019. CORE rank B. [ DOI | pdf | web ]

Andreas Nautsch, Jose Patino, Amos Treiber, Themos Stafylakis, Petr Mizera, Massimiliano Todisco, Thomas Schneider, and Nicholas Evans. Privacy-preserving speaker recognition with cohort score normalisation. In 20. Conference of the International Speech Communication Association (INTERSPEECH'19), pages 2868–2872, International Speech Communication Association (ISCA), Graz, Austria, September 15-19, 2019. Online: https://arxiv.org/abs/1907.03454. Acceptance rate 49.3%. CORE rank A. [ DOI | pdf | web ]

2018

Nikolaos P. Karvelas, Amos Treiber, and Stefan Katzenbeisser. Examining leakage of access counts in ORAM constructions. In 17. Workshop on Privacy in the Electronic Society (WPES'18), pages 71–75, ACM, Toronto, Canada, October 15, 2018. Acceptance rate 36.5%. [ DOI | web ]

Nikolaos P. Karvelas, Amos Treiber, and Stefan Katzenbeisser. Examining leakage of access counts in ORAM constructions. In 29. Workshop der Fachgruppe Kryptographie in der Gesellschaft für Informatik (Kryptotag), Bosch Renningen, Germany, September 6-7, 2018.

Amos Treiber. Access count leakage in oblivious RAMs. Master's thesis, TU Darmstadt, Germany, May, 2018.

2015

Amos Treiber. Searchable encryption. Bachelor's thesis, Universität Mannheim & Ruprecht-Karls Universität Heidelberg, Germany, December, 2015.