1 citations · 1 across the 1 of their papers we have counts for
3 papers
cs.CR2020★ 1 cited
Privacy-preserving collaborative machine learning on genomic data using TensorFlow
Cheng Hong, Zhicong Huang, Wen-jie Lu +4
Machine learning (ML) methods have been widely used in genomic studies. However, genomic data are often held by different stakeholders (e.g. hospitals, universities, and healthcare…
cs.LG2018
A generic framework for privacy preserving deep learning
Theo Ryffel, Andrew Trask, Morten Dahl +4
We detail a new framework for privacy preserving deep learning and discuss its assets. The framework puts a premium on ownership and secure processing of data and introduces a valu…
cs.CR2018
Private Machine Learning in TensorFlow using Secure Computation
Morten Dahl, Jason Mancuso, Yann Dupis +5
We present a framework for experimenting with secure multi-party computation directly in TensorFlow. By doing so we benefit from several properties valuable to both researchers and…