CORE A/A* ranked venues marked in bold.

2024

Vasisht Duddu, Anudeep Das, Nora Khayata, Hossein Yalame, Thomas Schneider, and N. Asokan. Attesting distributional properties of training data for machine learning. In 29. European Symposium on Research in Computer Security (ESORICS'24), LNCS, Springer, Bydgoszcz, Poland, September 16-20, 2024. To appear. Full version: https://arxiv.org/abs/2308.09552. Code: https://github.com/ssg-research/distribution-attestation. CORE rank A. [ pdf | web ]

Heiko Mantel, Joachim Schmidt, Thomas Schneider, Maximilian Stillger, Tim Weißmantel, and Hossein Yalame. HyCaMi: High-level synthesis for cache side mitigation. In 61. Design Automation Conference (DAC'24), ACM, San Francisco, CA, USA, June 23-27, 2024. To appear. Code: https://encrypto.de/code/HyCaMi. Online: https://ia.cr/2024/533. Acceptance rate 23%. CORE rank A. [ pdf | web ]

Andreas Brüggemann, Oliver Schick, Thomas Schneider, Ajith Suresh, and Hossein Yalame. Don't eject the impostor: Fast three-party computation with a known cheater. In 45. IEEE Symposium on Security and Privacy (IEEE S&P'24), IEEE, San Francisco, CA, USA, May 20-23, 2024. Full version: https://ia.cr/2023/1744. Acceptance rate 14.9%. CORE rank A*. [ DOI | pdf | web ]

Yaniv Ben-Itzhak, Helen Möllering, Benny Pinkas, Thomas Schneider, Ajith Suresh, Oleksandr Tkachenko, Shay Vargaftik, Christian Weinert, Hossein Yalame, and Avishay Yanai. ScionFL: Secure quantized aggregation for federated learning. In 2. IEEE Conference on Secure and Trustworthy Machine Learning (SaTML'24), pages 490–511, IEEE, Toronto, Canada, April 9-11, 2024. Runner-up Distinguished Paper Award. Online: https://arxiv.org/abs/2210.07376. Acceptance rate 21.5%. [ DOI | pdf | web ]

2023

Yann Disser, Daniel Günther, Thomas Schneider, Maximilian Stillger, Arthur Wigandt, and Hossein Yalame. Breaking the size barrier: Universal circuits meet lookup tables. In 29. Advances in Cryptology - ASIACRYPT'23, volume 14438 of LNCS, pages 3–37, Springer, Guangzhou, China, December 4-8, 2023. Full version: https://ia.cr/2022/1652. Code: https://encrypto.de/code/LUC. Acceptance rate 28.2%. CORE rank A. [ DOI | pdf | web ]

Thomas Schneider, Hossein Yalame, and Michael Yonli. Griffin: Towards mixed multi-key homomorphic encryption. In 20. International Conference on Security and Cryptography (SECRYPT'23), pages 147–158, SciTePress, Rome, Italy, July 10-12, 2023. Full version: https://ia.cr/2023/654. Acceptance rate 13.0% for full papers. CORE rank B. [ DOI | pdf | web ]

Till Gehlhar, Felix Marx, Thomas Schneider, Ajith Suresh, Tobias Wehrle, and Hossein Yalame. SafeFL: MPC-friendly framework for private and robust federated learning. In 6. Deep Learning Security and Privacy Workshop (DLSP'23), pages 69–76, IEEE, San Francisco, CA, USA, May 25, 2023. Full version: https://ia.cr/2023/555. [ DOI | pdf | web ]

Andreas Brüggemann, Robin Hundt, Thomas Schneider, Ajith Suresh, and Hossein Yalame. FLUTE: Fast and secure lookup table evaluations. In 44. IEEE Symposium on Security and Privacy (IEEE S&P'23), pages 515–533, IEEE, San Francisco, CA, USA, May 22-25, 2023. Full version: https://ia.cr/2023/499. Code: https://encrypto.de/code/FLUTE. Acceptance rate 17.0%. CORE rank A*. [ DOI | pdf | web ]

Andreas Brüggemann, Thomas Schneider, Ajith Suresh, and Hossein Yalame. Is everyone equally trustworthy in practice? (Short Talk). 44. IEEE Symposium on Security and Privacy (IEEE S&P'23) Short Talk, San Francisco, CA, USA, May 22-25, 2023. CORE rank A*. [ web ]

Thomas Schneider, Hossein Yalame, and Michael Yonli. POSTER: Towards mixed multi-key homomorphic encryption. 2. Annual FHE.org Conference on Fully Homomorphic Encryption (FHE.org'23) Poster Session, Tokyo, Japan, March 26, 2023. [ web ]

Felix Marx, Thomas Schneider, Ajith Suresh, Tobias Wehrle, Christian Weinert, and Hossein Yalame. HyFL: A Hybrid framework for private Federated Learning. arXiv:2302.09904, February 20, 2023. https://arxiv.org/abs/2302.09904. [ DOI ]

Thomas Schneider, Ajith Suresh, and Hossein Yalame. Comments on “Privacy-enhanced federated learning against poisoning adversaries”. IEEE Transactions on Information Forensics and Security (TIFS), 18:1407–1409, January 20, 2023. CORE rank A. [ DOI | pdf | web ]

2022

Andreas Brüggemann, Thomas Schneider, Ajith Suresh, and Hossein Yalame. POSTER: Efficient three-party shuffling using precomputation. In 29. ACM Conference on Computer and Communications Security (CCS'22) Posters/Demos, pages 3331–3333, ACM, Los Angeles, USA, November 7-11, 2022. CORE rank A*. [ DOI | pdf | web ]

Thien Duc Nguyen, Phillip Rieger, Huili Chen, Hossein Yalame, Helen Möllering, Hossein Fereidooni, Samuel Marchal, Markus Miettinen, Azalia Mirhoseini, Shaza Zeitouni, Farinaz Koushanfar, Ahmad-Reza Sadeghi, and Thomas Schneider. FLAME: Taming backdoors in federated learning. In 31. USENIX Security Symposium (USENIX Security'22), pages 1415–1432, USENIX, Boston, MA, USA, August 10-12, 2022. Online: https://ia.cr/2021/025. Acceptance rate 18.1%. CORE rank A*. [ pdf | web ]

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), pages 147–157, IEEE, Washington DC, USA, June 27-30, 2022. Full version: https://ia.cr/2021/1153. Acceptance rate 23%. [ DOI | pdf | web ]

2021

Arpita Patra, Thomas Schneider, Ajith Suresh, and Hossein Yalame. POSTER: ABY2.0: New efficient primitives for STPC with applications to privacy in machine learning (Extended Abstract). Privacy in Machine Learning Workshop (PriML@NeurIPS'21), Virtual Event, December 14, 2021. [ web ]

Aditya Hegde, Helen Möllering, Thomas Schneider, and Hossein Yalame. CONTRIBUTED TALK: SoK: Privacy-preserving clustering (Extended Abstract). Privacy in Machine Learning Workshop (PriML@NeurIPS'21), Virtual Event, December 14, 2021. [ 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), pages 131–145, ACM, Austin, TX, USA, December 6-10, 2021. Full version: https://ia.cr/2021/1493. Code: https://encrypto.de/code/VASA. Acceptance rate 21.0%. CORE rank A. [ DOI | pdf | web ]

Hannah Keller, Helen Möllering, Thomas Schneider, and Hossein Yalame. POSTER: 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. [ web ]

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

Arpita Patra, Thomas Schneider, Ajith Suresh, and Hossein Yalame. POSTER: 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. [ 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 (Extended Abstract). 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% for full papers. 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 (Abstract). In 32. Kryptotag (crypto day matters), Gesellschaft für Informatik e.V. / FG KRYPTO, Virtual Event, January 15, 2021. [ DOI | pdf ]

2020

Fabian Boemer, Rosario Cammarota, Daniel Demmler, Thomas Schneider, and Hossein Yalame. POSTER: MP2ML: A mixed-protocol machine learning framework for private inference (Extended Abstract). Privacy Preserving Machine Learning Workshop (PPML@NeurIPS'20), Virtual Event, December 11, 2020. [ 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. Acceptance rate 47.1%. [ 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 (Extended Abstract). 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.