62 citations · 95 across the 4 of their papers we have counts for
4 papers
CrypTen: Secure Multi-Party Computation Meets Machine Learning
Brian Knott, Shobha Venkataraman, Awni Hannun +3
Secure multi-party computation (MPC) allows parties to perform computations on data while keeping that data private. This capability has great potential for machine-learning applic…
CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU
Sijun Tan, Brian Knott, Yuan Tian +1
We introduce CryptGPU, a system for privacy-preserving machine learning that implements all operations on the GPU (graphics processing unit). Just as GPUs played a pivotal role in…
Secure multiparty computations in floating-point arithmetic
Chuan Guo, Awni Hannun, Brian Knott +3
Secure multiparty computations enable the distribution of so-called shares of sensitive data to multiple parties such that the multiple parties can effectively process the data whi…
Privacy-Preserving Multi-Party Contextual Bandits
Awni Hannun, Brian Knott, Shubho Sengupta +1
Contextual bandits are online learners that, given an input, select an arm and receive a reward for that arm. They use the reward as a learning signal and aim to maximize the total…