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Found 9 papers, 0 papers with code
HyFL: A Hybrid Framework For Private Federated Learning
Federated learning (FL) has emerged as an efficient approach for large-scale distributed machine learning, ensuring data privacy by keeping training data on client devices.
ScionFL: Efficient and Robust Secure Quantized Aggregation
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.
MPClan: Protocol Suite for Privacy-Conscious Computations
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.
MPCLeague: Robust MPC Platform for Privacy-Preserving Machine Learning
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.
Tetrad: Actively Secure 4PC for Secure Training and Inference
A key feature of Tetrad is that robustness comes for free over fair protocols.
SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning
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.
BLAZE: Blazing Fast Privacy-Preserving Machine Learning
This motivated the area of Privacy-preserving Machine Learning (PPML) where privacy of the data is guaranteed.
ASTRA: High Throughput 3PC over Rings with Application to Secure Prediction
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.
Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning
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).
