CryptoNite: Revealing the Pitfalls of End-to-End Private Inference at
Scale

CryptoNite: Revealing the Pitfalls of End-to-End Private Inference at Scale

4 November 2021

Karthik Garimella

Papers citing "CryptoNite: Revealing the Pitfalls of End-to-End Private Inference at Scale"

6 / 6 papers shown

Title
Approximating ReLU on a Reduced Ring for Efficient MPC-based Private Inference Kiwan Maeng G. E. Suh 30 2 0 09 Sep 2023
Privacy Preserving In-memory Computing Engine Haoran Geng Jianqiao Mo D. Reis Jonathan Takeshita Taeho Jung Brandon Reagen M. Niemier X. Sharon Hu 42 0 0 04 Aug 2023
Characterizing and Optimizing End-to-End Systems for Private Inference Karthik Garimella Zahra Ghodsi N. Jha S. Garg Brandon Reagen 31 25 0 14 Jul 2022
CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU Sijun Tan Brian Knott Yuan Tian David J. Wu BDL FedML 57 183 0 22 Apr 2021
CrypTFlow2: Practical 2-Party Secure Inference Deevashwer Rathee Mayank Rathee Nishant Kumar Nishanth Chandran Divya Gupta Aseem Rastogi Rahul Sharma 79 301 0 13 Oct 2020
Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware Florian Tramèr Dan Boneh FedML 114 395 0 08 Jun 2018