7 citations · 9 across the 3 of their papers we have counts for
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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…