no code implementations • 30 Sep 2024 • Thomas Schneider, Ajith Suresh, Hossein Yalame
al., IEEE TIFS'23), several subsequent papers continued to reference Liu et al. (IEEE TIFS'21) as a potential solution for private federated learning.
no code implementations • 20 Feb 2023 • Felix Marx, Thomas Schneider, Ajith Suresh, Tobias Wehrle, Christian Weinert, Hossein Yalame
Federated learning (FL) is an efficient approach for large-scale distributed machine learning that promises data privacy by keeping training data on client devices.
no code implementations • 13 Oct 2022 • Yaniv Ben-Itzhak, Helen Möllering, Benny Pinkas, Thomas Schneider, Ajith Suresh, Oleksandr Tkachenko, Shay Vargaftik, Christian Weinert, Hossein Yalame, Avishay Yanai
In this paper, we unite both research directions by introducing ScionFL, the first secure aggregation framework for FL that operates efficiently on quantized inputs and simultaneously provides robustness against malicious clients.
no code implementations • 24 Jun 2022 • Nishat Koti, Shravani Patil, Arpita Patra, Ajith Suresh
In addition to having an improved online communication cost, we can shut down almost half of the parties in the online phase, thereby saving up to 50% in the system's operational costs.
no code implementations • 26 Dec 2021 • Ajith Suresh
In this thesis, we design an efficient platform, MPCLeague, for PPML in the Secure Outsourced Computation (SOC) setting using Secure Multi-party Computation (MPC) techniques.
no code implementations • 5 Jun 2021 • Nishat Koti, Arpita Patra, Rahul Rachuri, Ajith Suresh
A key feature of Tetrad is that robustness comes for free over fair protocols.
no code implementations • 20 May 2020 • Nishat Koti, Mahak Pancholi, Arpita Patra, Ajith Suresh
In this work, we propose SWIFT, a robust PPML framework for a range of ML algorithms in SOC setting, that guarantees output delivery to the users irrespective of any adversarial behaviour.
no code implementations • 18 May 2020 • Arpita Patra, Ajith Suresh
This motivated the area of Privacy-preserving Machine Learning (PPML) where privacy of the data is guaranteed.
no code implementations • 5 Dec 2019 • Harsh Chaudhari, Rahul Rachuri, Ajith Suresh
Machine learning has started to be deployed in fields such as healthcare and finance, which propelled the need for and growth of privacy-preserving machine learning (PPML).
no code implementations • 5 Dec 2019 • Harsh Chaudhari, Ashish Choudhury, Arpita Patra, Ajith Suresh
In this work, we present concretely-efficient protocols for secure $3$-party computation (3PC) over a ring of integers modulo $2^{\ell}$ tolerating one corruption, both with semi-honest and malicious security.