CORE A/A* ranked venues marked in bold.

2022

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 ]

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 ]

2020

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 ]