Private Machine Learning in TensorFlow using Secure Computation

Private Machine Learning in TensorFlow using Secure Computation

18 October 2018

Jason V. Mancuso

Ian Livingstone

Justin Patriquin

Papers citing "Private Machine Learning in TensorFlow using Secure Computation"

10 / 10 papers shown

Title
HawkEye: Statically and Accurately Profiling the Communication Cost of Models in Multi-party Learning Wenqiang Ruan Xin Lin Ruisheng Zhou Guopeng Lin Shui Yu Weili Han 47 0 0 16 Feb 2025
Efficient ML Models for Practical Secure Inference Vinod Ganesan Anwesh Bhattacharya Pratyush Kumar Divya Gupta Rahul Sharma Nishanth Chandran MedIm 59 5 0 26 Aug 2022
Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy Wenqiang Ruan Ming Xu Wenjing Fang Li Wang Lei Wang Wei Han 40 12 0 18 Aug 2022
Towards General-purpose Infrastructure for Protecting Scientific Data Under Study Andrew Trask Kritika Prakash 43 3 0 04 Oct 2021
Secure Medical Image Analysis with CrypTFlow Javier Alvarez-Valle Pratik Bhatu Nishanth Chandran Divya Gupta A. Nori Aseem Rastogi Mayank Rathee Rahul Sharma Shubham Ugare MedIm 18 13 0 09 Dec 2020
A Scalable Approach for Privacy-Preserving Collaborative Machine Learning Jinhyun So Başak Güler A. Avestimehr FedML 12 50 0 03 Nov 2020
Secure Evaluation of Quantized Neural Networks Anders Dalskov Daniel E. Escudero Marcel Keller 17 137 0 28 Oct 2019
SEALion: a Framework for Neural Network Inference on Encrypted Data Tim van Elsloo Giorgio Patrini Hamish Ivey-Law FedML 18 42 0 29 Apr 2019
CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning Jinhyun So Başak Güler A. Avestimehr FedML 19 114 0 02 Feb 2019
nGraph-HE: A Graph Compiler for Deep Learning on Homomorphically Encrypted Data Fabian Boemer Yixing Lao Rosario Cammarota Casimir Wierzynski FedML 13 163 0 23 Oct 2018