Research
Click here for more information about the publications, including links to full versions.
Preprints
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HyFL: A Hybrid Approach For Private Federated Learning.
Authors: Felix Marx, Thomas Schneider, Ajith Suresh, Tobias Wehrle, Christian Weinert and Hossein Yalame.
(Under Submission)
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ScionFL: Secure Quantized Aggregation for Federated Learning.
Authors: Yaniv Ben-Itzhak, Helen Möllering, Benny Pinkas, Thomas Schneider, Ajith Suresh, Oleksandr Tkachenko, Shay Vargaftik, Christian Weinert, Hossein Yalame and Avishay Yanai.
(Under Submission)
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Privacy‑Preserving Epidemiological Modeling on Mobile Graphs.
Authors: Daniel Günther, Marco Holz, Benjamin Judkewitz, Helen Möllering, Benny Pinkas, Thomas Schneider and Ajith Suresh.
(Under Submission)
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Privadome: Protecting Citizen Privacy from Delivery Drones.
Authors: Vinod Ganapathy, Eikansh Gupta, Arpita Patra, Gokulnath Pillai and Ajith Suresh.
(Under Submission)
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MPClan: Protocol Suite for Privacy-Conscious Computations.
Authors: Nishat Koti, Shravani Patil, Arpita Patra and Ajith Suresh.
(Under Submission)
Publications
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Comments on “Privacy‑Enhanced Federated Learning Against Poisoning Adversaries”.
Authors: Thomas Schneider, Ajith Suresh and Hossein Yalame.
IEEE TIFS'23 [CORE - A]
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Tetrad: Fair and Robust 4PC Framework for Privacy-Preserving Machine Learning.
Authors: Nishat Koti, Arpita Patra, Rahul Rachuri and Ajith Suresh.
NDSS'22 [CORE - A*]
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SynCirc: Efficient Synthesis of Depth‑Optimized Circuits for Secure Computation.
Authors: Arpita Patra, Thomas Schneider, Ajith Suresh and Hossein Yalame.
IEEE HOST'21
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SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning.
Authors: Nishat Koti, Mahak Pancholi, Arpita Patra and Ajith Suresh.
USENIX Security'21 [CORE - A*]
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ABY2.0: Improved Mixed-Protocol Secure Two-Party Computation.
Authors: Arpita Patra, Thomas Schneider, Ajith Suresh and Hossein Yalame.
USENIX Security'21 [CORE - A*]
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BLAZE: Blazing Fast Privacy-Preserving Machine Learning.
Authors: Arpita Patra and Ajith Suresh.
NDSS'20 [CORE - A*]
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Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning.
Authors: Harsh Chaudhari, Rahul Rachuri and Ajith Suresh.
NDSS'20 [CORE - A*]
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FLASH: Fast and Robust Framework for Privacy-preserving Machine Learning.
Authors: Megha Byali, Harsh Chaudhari, Arpita Patra and Ajith Suresh.
PoPETS'20 [CORE - A]
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ASTRA: High Throughput 3PC over Rings with Application to Secure Prediction.
Authors: Harsh Chaudhari, Ashish Choudhury, Arpita Patra and Ajith Suresh.
ACM CCSW'19
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Fast Actively Secure OT Extension for Short Secrets.
Authors: Arpita Patra, Pratik Sarkar and Ajith Suresh.
NDSS'20 [CORE - A*]
Workshops, Symposiums & Posters
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MPCLeague: Robust MPC Platform for Privacy-Preserving Machine Learning.
Authors: Ajith Suresh.
Doctoral Symposium (AIMLSystems'22)
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Efficient Three-Party Shuffling Using Precomputation.
Authors: Andreas Brüggemann, Thomas Schneider, Ajith Suresh and Hossein Yalame.
ACM CCS'22 (Poster)
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Privacy-Preserving Epidemiological Modeling on Mobile Graphs.
Authors: Daniel Günther, Marco Holz, Benjamin Judkewitz, Helen Möllering, Benny Pinkas, Thomas Schneider and Ajith Suresh.
ACM CCS'22 (Poster)
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MPClan: Protocol Suite for Privacy-Conscious Computations.
Authors: Nishat Koti, Shravani Patil, Arpita Patra and Ajith Suresh.
ACM CCS'22 (Poster), NDSS'22 (Poster)
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Tetrad: Fair and Robust 4PC Framework for Privacy-Preserving Machine Learning.
Authors: Nishat Koti, Arpita Patra, Rahul Rachuri and Ajith Suresh.
PPML'21 (ACM CCS'21 Workshop)
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ABY2.0: Improved Mixed-Protocol Secure Two-Party Computation.
Authors: Arpita Patra, Thomas Schneider, Ajith Suresh and Hossein Yalame.
PriML'21 (NeurIPS'21 Workshop), PPML'21 (ACM CCS'21 Workshop)
PPML'21 (CRYPTO'21 Workshop)
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MPCLeague: Robust and Efficient Mixed-protocol Framework for 4-party Computation.
Authors: Nishat Koti, Arpita Patra and Ajith Suresh.
IEEE S&P 2021 (Poster), DPML'21 (ICLR Workshop 2021)
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SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning.
Authors: Nishat Koti, Mahak Pancholi, Arpita Patra and Ajith Suresh.
ARCS'22 (Symposium), DPML'21 (ICLR Workshop 2021)
PriML/PPML'20 (NeurIPS 2020 Workshop)
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ASTRA: High Throughput 3PC over Rings with Application to Secure Prediction.
Authors: Harsh Chaudhari, Ashish Choudhury, Arpita Patra and Ajith Suresh.
PPML'19 (ACM CCS'19 Workshop)