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Found 5 papers, 0 papers with code
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.
BLAZE: Blazing Fast Privacy-Preserving Machine Learning
This motivated the area of Privacy-preserving Machine Learning (PPML) where privacy of the data is guaranteed.
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.
Tetrad: Actively Secure 4PC for Secure Training and Inference
A key feature of Tetrad is that robustness comes for free over fair protocols.
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.
