Privacy-Preserving Machine Learning in Untrusted Clouds Made Simple

no code implementations • 9 Sep 2020 • Dayeol Lee, Dmitrii Kuvaiskii, Anjo Vahldiek-Oberwagner, Mona Vij

We present a practical framework to deploy privacy-preserving machine learning (PPML) applications in untrusted clouds based on a trusted execution environment (TEE).

BIG-bench Machine Learning Privacy Preserving

FireSim: FPGA-Accelerated Cycle-Exact Scale-Out System Simulation in the Public Cloud

1 code implementation • 45th ACM/IEEE International Symposium on Computer Architecture (ISCA 2018) 2018 • Sagar Karandikar, Howard Mao, Donggyu Kim, David Biancolin, Alon Amid, Dayeol Lee, Nathan Pemberton, Emmanuel Amaro, Colin Schmidt, Aditya Chopra, Qijing Huang, Kyle Kovacs, Borivoje Nikolic, Randy Katz, Jonathan Bachrach, Krste Asanovic

We present FireSim, an open-source simulation platform that enables cycle-exact microarchitectural simulation of large scale-out clusters by combining FPGA-accelerated simulation of silicon-proven RTL designs with a scalable, distributed network simulation.