CORE A/A* ranked venues marked in bold.

2021

Arpita Patra, Thomas Schneider, Ajith Suresh, and Hossein Yalame. ABY2.0: Improved mixed-protocol secure two-party computation. In 30. USENIX Security Symposium (USENIX Security'21), USENIX, Virtual Event, August 11-13, 2021. To appear. Full version: https://ia.cr/2020/1225. CORE rank A*. [ pdf | web ]

Hannah Keller, Helen Möllering, Thomas Schneider, and Hossein Yalame. Balancing quality and efficiency in private clustering with affinity propagation. In 18. International Conference on Security and Cryptography (SECRYPT'21), SciTePress, Virtual Event, July 6-8, 2021. CORE rank B. [ web ]

Tim Heldmann, Thomas Schneider, Oleksandr Tkachenko, Christian Weinert, and Hossein Yalame. LLVM-based circuit compilation for practical secure computation. In 19. International Conference on Applied Cryptography and Network Security (ACNS'21), LNCS, Springer, Virtual Event, June 21-24, 2021. To appear. Acceptance rate 20.9%. CORE rank B. [ web ]

Hossein Fereidooni, Samuel Marchal, Markus Miettinen, Azalia Mirhoseini, Helen Möllering, Thien Duc Nguyen, Phillip Rieger, Ahmad-Reza Sadeghi, Thomas Schneider, Hossein Yalame, and Shaza Zeitouni. SAFELearn: Secure aggregation for private federated learning. In 4. Deep Learning and Security Workshop (DLS'21), IEEE, Virtual Event, May 27, 2021. To appear. Full version: https://ia.cr/2021/386. [ web ]

Hannah Keller, Helen Möllering, Thomas Schneider, and Hossein Yalame. Privacy-preserving clustering. In 32. Kryptotag (crypto day matters), Virtual Event, January 15, 2021. [ DOI | pdf ]

Thien Duc Nguyen, Phillip Rieger, Hossein Yalame, Helen Möllering, Hossein Fereidooni, Samuel Marchal, Markus Miettinen, Azalia Mirhoseini, Ahmad-Reza Sadeghi, Thomas Schneider, and Shaza Zeitouni. FLGUARD: Secure and private federated learning, Jan 6, 2021. https://ia.cr/2021/025.

2020

Fabian Boemer, Rosario Cammarota, Daniel Demmler, Thomas Schneider, and Hossein Yalame. MP2ML: A mixed-protocol machine learning framework for private inference. Privacy Preserving Machine Learning (PPML@NeurIPS'20) - PriML and PPML Joint Edition – NeurIPS'20 Workshop, Virtual Event, December 11, 2020. Poster presentation. [ web ]

Fabian Boemer, Rosario Cammarota, Daniel Demmler, Thomas Schneider, and Hossein Yalame. MP2ML: A mixed-protocol machine learning framework for private inference (Extended Abstract). In Privacy-Preserving Machine Learning in Practice (PPMLP@CCS'20) – CCS'20 Workshop, pages 43–45, ACM, Virtual Event, November 9, 2020. Short paper. [ DOI | pdf | web ]

Fabian Boemer, Rosario Cammarota, Daniel Demmler, Thomas Schneider, and Hossein Yalame. MP2ML: A mixed-protocol machine learning framework for private inference. In 15. International Conference on Availability, Reliability and Security (ARES'20), pages 14:1–14:10, ACM, Virtual Event, August 25-28, 2020. Full version: https://ia.cr/2020/721. Code: https://github.com/IntelAI/he-transformer. Acceptance rate 21.3%. CORE rank B. [ DOI | pdf | web ]

Mohammad Haji Seyed Javadi, Hossein Yalame, and Hamid Reza Mahdiani. Small constant mean-error imprecise adder/multiplier for efficient VLSI implementation of mac-based applications. IEEE Transactions on Computers, 69(9):1376–1387, 2020. CORE rank A*. [ DOI | web ]

2017

Hossein Yalame, Mohammad Hossein Farzam, and Siavash Bayat Sarmadi. Secure two-party computation using an efficient garbled circuit by reducing data transfer. In 8. International Conference on Applications and Techniques in Information Security (ATIS'17), pages 23–34, Springer, Auckland, New Zealand, July 6-7, 2017. [ DOI | web ]

Hossein Yalame. An efficient secure two-party computation with a combination of GC and GMW. Master's thesis, Sharif University of Technology, Iran, 2017.

2015

Hossein Yalame. Bio-inspired imprecise adder/multiplier for efficient implementation of MAC-based applications. Bachelor's thesis, Shahid Beheshti University, Iran, 2015.