Ajith Suresh

I am a chronic talker and lifelong learner who enjoys what I do. I was born in a remote village in Kerala, India's beautiful state known as "God's Own Country." My parents instilled in me the importance of following my dreams. I earned a Bachelor of Science in Computer Science and Engineering (CSE) from the College of Engineering (CET), Trivandrum. I pursued my passion at the Indian Institute of Science (IISc), where I earned my Masters (Research) and Ph.D in CSE under the guidance of Prof. Arpita Patra. During my doctoral studies, I investigated the field of MPC for a small population, with applications to Privacy-Preserving Machine Learning (PPML).

I am a Senior MPC Researcher at The Technology Innovation Institute (TII), Abu Dhabi, and I am affiliated with the Cryptography Research Center at TII. Previously, I conducted 1.5 years of post-doctoral research at the Cryptography and Privacy Engineering (ENCRYPTO) group at the Department of Computer Science at the Technical University of Darmstadt under the supervision of Prof. Thomas Schneider. My research orbits around the the design and development of applied MPC protocols, alongside exploring the fields of privacy-preserving machine learning and federated learning.

I have been awarded a Google PhD Fellowship for 2019. When you're bored, go to World Through My Eyes.

Experience

Education

Research

Click here for more information about the publications, including links to full versions.

• HyFL: A Hybrid Approach For Private Federated Learning.
  Authors: Felix Marx, Thomas Schneider, Ajith Suresh, Tobias Wehrle, Christian Weinert and Hossein Yalame.
  (Under Submission)
• 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)
• 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)

• Privadome: Delivery Drones and Citizen Privacy.
  Authors: Vinod Ganapathy, Eikansh Gupta, Arpita Patra, Gokulnath Pillai and Ajith Suresh.
  PETS'24 [CORE - A]
• Don't Eject the Impostor: Fast Three-Party Computation With a Known Cheater.
  Authors: Andreas Brüggemann, Oliver Schick, Thomas Schneider, Ajith Suresh and Hossein Yalame.
  IEEE S&P'24 [CORE - A*]
• PrivMail: A Privacy-Preserving Framework for Secure Emails.
  Authors: Gowri R Chandran, Raine Nieminen, Thomas Schneider and Ajith Suresh.
  ESORICS'23 [CORE - A]
• MPClan: Protocol Suite for Privacy-Conscious Computations.
  Authors: Nishat Koti, Shravani Patil, Arpita Patra and Ajith Suresh.
  Journal of Cryptology (JoC'23) [CORE - A*]
• FLUTE: Fast and Secure Lookup Table Evaluations.
  Authors: Andreas Brüggemann, Robin Hundt, Thomas Schneider, Ajith Suresh and Hossein Yalame.
  IEEE S&P'23 [CORE - A*]
• SafeFL: MPC‐friendly framework for Private and Robust Federated Learning.
  Authors: Till Gehlhar, Felix Marx, Thomas Schneider, Ajith Suresh, Tobias Wehrle and Hossein Yalame.
  DLSP @ IEEE S&P'23
• Comments on “Privacy‑Enhanced Federated Learning Against Poisoning Adversaries”.
  Authors: Thomas Schneider, Ajith Suresh and Hossein Yalame.
  IEEE TIFS'23 [CORE - A]
• 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*]
• SynCirc: Efficient Synthesis of Depth‑Optimized Circuits for Secure Computation.
  Authors: Arpita Patra, Thomas Schneider, Ajith Suresh and Hossein Yalame.
  IEEE HOST'21
• SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning.
  Authors: Nishat Koti, Mahak Pancholi, Arpita Patra and Ajith Suresh.
  USENIX Security'21 [CORE - A*]
• ABY2.0: Improved Mixed-Protocol Secure Two-Party Computation.
  Authors: Arpita Patra, Thomas Schneider, Ajith Suresh and Hossein Yalame.
  USENIX Security'21 [CORE - A*]
• BLAZE: Blazing Fast Privacy-Preserving Machine Learning.
  Authors: Arpita Patra and Ajith Suresh.
  NDSS'20 [CORE - A*]
• Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning.
  Authors: Harsh Chaudhari, Rahul Rachuri and Ajith Suresh.
  NDSS'20 [CORE - A*]
• FLASH: Fast and Robust Framework for Privacy-preserving Machine Learning.
  Authors: Megha Byali, Harsh Chaudhari, Arpita Patra and Ajith Suresh.
  PETS'20 [CORE - A]
• ASTRA: High Throughput 3PC over Rings with Application to Secure Prediction.
  Authors: Harsh Chaudhari, Ashish Choudhury, Arpita Patra and Ajith Suresh.
  ACM CCSW'19
• Fast Actively Secure OT Extension for Short Secrets.
  Authors: Arpita Patra, Pratik Sarkar and Ajith Suresh.
  NDSS'20 [CORE - A*]

Awards & Certifications

• Awarded commendation certificate for outstanding PhD thesis by Deaprtment of Computer Science and Automation at the Indian Institute of Science (IISc), Bangalore.
• Represented India in the "Window to the World" session at the 8th Heidelberg Laureate Forum.
• Selected as one among 225 young researchers to participate in the 8th Heidelberg Laureate Forum.
• Recipient of Google PhD Fellowship 2019.
• Organiser of Secure Multi-Party Computation: Theory and Practice Workshop at IISc (January, 2020).
• Received travel grant to attend Privacy Preserving Machine Learning Workshop, CCS 2019, London.
• Received travel grant to attend Workshop: Theory and Practice of Secure Multiparty Computation 2016, Aarhus University, Denmark.
• Secured All India Rank of 807 with a score of 688 in GATE 2014 among (approximately) 1,55,190 students in India.
• Best Outgoing student in Computer Science and Engineering (P Rathnaswamy Memorial Endowment) for the year 2014.
• First prize in Coding Competition, CODESTORM, conducted by IEEE Computer Society (March 2013).
• Received Central Sector Scholarship by Government of India (August 2010).
• Received Malayala Manorama Merit Scholarship (February 2008).

Curriculum vitae

My CV is available here - [CV] [CV-Short].