CORE A/A* ranked venues marked in bold.

2024

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

Gowri R Chandran, Raine Nieminen, Thomas Schneider, and Ajith Suresh. PrivMail: A privacy-preserving framework for secure emails. In 28. European Symposium on Research in Computer Security (ESORICS'23), volume 14345 of LNCS, pages 145–165, Springer, The Hague, The Netherlands, September 25-29, 2023. Full version: https://ia.cr/2023/1294. Code: https://encrypto.de/code/PrivMail. Acceptance rate 19.5%. CORE rank A. [ 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 ]

Gowri R Chandran, Raine Nieminen, Thomas Schneider, and Ajith Suresh. PrivMail: a privacy-preserving framework for secure emails (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 ]

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 ]

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 ]

Nishat Koti, Shravani Patil, Arpita Patra, and Ajith Suresh. MPClan: Protocol suite for privacy-conscious computations. Journal of Cryptology, 36(3):22, 2023. CORE rank A*. [ pdf ]

2022

Nishat Koti, Shravani Patil, Arpita Patra, and Ajith Suresh. POSTER: MPClan: Protocol suite for privacy-conscious computations. In 29. ACM Conference on Computer and Communications Security (CCS'22) Posters/Demos, pages 3379–3381, ACM, Los Angeles, USA, November 7-11, 2022. CORE rank A*. [ DOI | pdf | web ]

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 ]

Daniel Günther, Marco Holz, Benjamin Judkewitz, Helen Möllering, Benny Pinkas, Thomas Schneider, and Ajith Suresh. POSTER: Privacy-preserving epidemiological modeling on mobile graphs. In 29. ACM Conference on Computer and Communications Security (CCS'22) Posters/Demos, pages 3351–3353, ACM, Los Angeles, USA, November 7-11, 2022. CORE rank A*. [ DOI | 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 ]

Nishat Koti, Arpita Patra, Rahul Rachuri, and Ajith Suresh. Tetrad: Actively secure 4PC for secure training and inference. In 29. Network and Distributed System Security Symposium (NDSS'22), Internet Society, San Diego, CA, USA, April 24-28, 2022. CORE rank A*. [ pdf | web ]

Nishat Koti, Shravani Patil, Arpita Patra, and Ajith Suresh. POSTER: MPClan: Protocol Suite for Privacy-Conscious Computations. 29. Network and Distributed System Security Symposium (NDSS'22) Poster Session, San Diego, CA, USA, April 24-28, 2022. CORE rank A*. [ web ]

Nishat Koti, Arpita Patra, Rahul Rachuri, and Ajith Suresh. Tetrad: Actively secure 4PC for secure training and inference. In Network and Distributed System Security Symposium (NDSS'22), Internet Society, 2022. CORE rank A*. [ 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 ]

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 ]

Nishat Koti, Mahak Pancholi, Arpita Patra, and Ajith Suresh. SWIFT: Super-fast and robust privacy-preserving machine learning. In 30. USENIX Security Symposium (USENIX Security'21), pages 2651–2668, USENIX, Virtual Event, August 11-13 2021. Acceptance rate 19%. CORE rank A*. [ pdf ]

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 ]

Ajith Suresh. MPCLeague: Robust MPC Platform for Privacy-Preserving Machine Learning. PhD thesis, Indian Institute of Science, Bangalore, July 28, 2021. [ pdf ]

Nishat Koti, Mahak Pancholi, Arpita Patra, and Ajith Suresh. SWIFT: Super-fast and robust privacy-preserving machine learning. In USENIX Security Symposium (USENIX Security'21), pages 2651–2668, USENIX, 2021. CORE rank A*. [ pdf ]

2020

Daniel Günther, Marco Holz, Benjamin Judkewitz, Helen Möllering, Benny Pinkas, Thomas Schneider, and Ajith Suresh. Privacy-preserving epidemiological modeling on mobile graphs. Cryptology ePrint Archive, Report 2020/1546, December 11, 2020. https://ia.cr/2020/1546.

Arpita Patra and Ajith Suresh. BLAZE: Blazing fast privacy-preserving machine learning. In 27. Network and Distributed System Security Symposium (NDSS'20), Internet Society, San Francisco, CA, USA, February 23-26, 2020. CORE rank A*. [ DOI | pdf ]

Harsh Chaudhari, Rahul Rachuri, and Ajith Suresh. Trident: Efficient 4PC framework for privacy preserving machine learning. In 27. Network and Distributed System Security Symposium (NDSS'20), Internet Society, San Francisco, CA, USA, February 23-26, 2020. CORE rank A*. [ DOI | pdf ]

Megha Byali, Harsh Chaudhari, Arpita Patra, and Ajith Suresh. FLASH: Fast and robust framework for privacy-preserving machine learning. Proceedings on Privacy Enhancing Technologies (PoPETs), 2020(2):459–480, 2020. CORE rank A. [ DOI | pdf ]

2019

Harsh Chaudhari, Ashish Choudhury, Arpita Patra, and Ajith Suresh. ASTRA: High throughput 3PC over rings with application to secure prediction. In 10. ACM Cloud Computing Security Workshop (CCSW'19), pages 81–92, ACM, London, UK, November 11, 2019. [ DOI | pdf ]

2017

Arpita Patra, Pratik Sarkar, and Ajith Suresh. Fast actively secure OT extension for short secrets. In 24. Network and Distributed System Security Symposium (NDSS'17), Internet Society, February 26 - March 1, 2017. CORE rank A*. [ DOI | pdf ]

Ajith Suresh. Fast actively secure ot extension for short secrets. Master's thesis, Indian Institute of Science, Bangalore, 2017. [ pdf ]