Team
Hossein Yalame

Hossein Yalame M.Sc.

Doctoral Researcher

Contact

work +49 6151 16-27301

Work S2|20 210
Pankratiusstraße 2
64289 Darmstadt

I am a doctoral researcher and member of the Cryptography and Privacy Engineering Group (ENCRYPTO) at the Department of Computer Science of Technische Universität Darmstadt, Germany.

My research focuses on compilers for cryptographic protocols.

Honors and Awards

Work Experience

Studies

Publications

CORE A/A* ranked venues marked in bold.

2021

Arpita Patra, Thomas Schneider, Ajith Suresh, and Hossein Yalame. SynCirc: Efficient synthesis of depth-optimized circuits for secure computation. In 14. IEEE International Workshop on Hardware-Oriented Security and Trust (HOST'21), IEEE, Washington DC, USA, December 12-15, 2021. To appear. Full version: https://ia.cr/2021/1153. [ pdf | web ]

Jean-Pierre Münch, Thomas Schneider, and Hossein Yalame. VASA: Vector AES instructions for Security Applications. In 37. Annual Computer Security Applications Conference (ACSAC'21), ACM, Austin, TX, USA, December 6-10, 2021. To appear. [ web ]

Hannah Keller, Helen Möllering, Thomas Schneider, and Hossein Yalame. Balancing quality and efficiency in private clustering with affinity propagation (Extended Abstract). 4. Privacy Preserving Machine Learning Workshop (PPML@CCS'21), Virtual Event, November 19, 2021. Virtual Poster. To appear. [ web ]

Aditya Hegde, Helen Möllering, Thomas Schneider, and Hossein Yalame. SoK: privacy-preserving clustering (Extended Abstract). 4. Privacy Preserving Machine Learning Workshop (PPML@CCS'21), Virtual Event, November 19, 2021. Virtual Poster. To appear. [ web ]

Arpita Patra, Thomas Schneider, Ajith Suresh, and Hossein Yalame. ABY2.0: New efficient primitives for 2PC with applications to privacy preserving machine learning (Extended Abstract). 4. Privacy Preserving Machine Learning Workshop (PPML@CCS'21), Virtual Event, November 19, 2021. Virtual Poster. To appear. [ web ]

Arpita Patra, Thomas Schneider, Ajith Suresh, and Hossein Yalame. ABY2.0: Improved mixed-protocol secure two-party computation with applications to privacy preserving machine learning (Contributed Talk). 3. Privacy-Preserving Machine Learning Workshop (PPML@CRYPTO'21), August 15, 2021. [ web ]

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), pages 2165–2182, USENIX, Virtual Event, August 11-13, 2021. Full version: https://ia.cr/2020/1225. Acceptance rate 19%. 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), pages 173–184, SciTePress, Virtual Event, July 6-8, 2021. Full version: https://ia.cr/2021/825. Code: https://encrypto.de/code/ppAffinityPropagation. Acceptance rate 18.4%. CORE rank B. [ DOI | pdf | web ]

Aditya Hegde, Helen Möllering, Thomas Schneider, and Hossein Yalame. SoK: Efficient privacy-preserving clustering. Proceedings on Privacy Enhancing Technologies (PoPETs), 2021(4):225–248, July 2021. Online: https://ia.cr/2021/809. Code: https://encrypto.de/code/SoK_ppClustering. Acceptance rate 19.5%. CORE rank A. [ DOI | pdf | 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), volume 12727 of LNCS, pages 99–121, Springer, Virtual Event, June 21-24, 2021. Online: https://ia.cr/2021/797. Code: https://encrypto.de/code/LLVM. Acceptance rate 19.9%. CORE rank B. [ DOI | pdf | 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), pages 56–62, IEEE, Virtual Event, May 27, 2021. Full version: https://ia.cr/2021/386. Acceptance rate 40%. [ DOI | pdf | web ]

Hannah Keller, Helen Möllering, Thomas Schneider, and Hossein Yalame. Privacy-preserving clustering. In 32. Kryptotag (crypto day matters), Gesellschaft für Informatik e.V. / FG KRYPTO, 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. POSTER: MP2ML: A mixed-protocol machine learning framework for private inference. Privacy Preserving Machine Learning Workshop (PPML@NeurIPS'20), 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 Workshop (PPMLP@CCS'20), 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 ]

Fabian Boemer, Rosario Cammarota, Daniel Demmler, Thomas Schneider, and Hossein Yalame. MP2ML: A mixed-protocol machine learning framework for private inference (Contributed Talk). 2. Privacy-Preserving Machine Learning Workshop (PPML@CRYPTO'20), August 16, 2020. [ 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.