76 citations · 90 across the 4 of their papers we have counts for
4 papers
Secure Quantized Training for Deep Learning
Marcel Keller, Ke Sun
We implement training of neural networks in secure multi-party computation (MPC) using quantization commonly used in said setting. We are the first to present an MNIST classifier p…
Effectiveness of MPC-friendly Softmax Replacement
Marcel Keller, Ke Sun
Softmax is widely used in deep learning to map some representation to a probability distribution. As it is based on exp/log functions that are relatively expensive in multi-party c…
A Note on Our Submission to Track 4 of iDASH 2019
Marcel Keller, Ke Sun
iDASH is a competition soliciting implementations of cryptographic schemes of interest in the context of biology. In 2019, one track asked for multi-party computation implementatio…
Secure Evaluation of Quantized Neural Networks
Anders Dalskov, Daniel Escudero, Marcel Keller
We investigate two questions in this paper: First, we ask to what extent "MPC friendly" models are already supported by major Machine Learning frameworks such as TensorFlow or PyTo…