Gazelle: A Low Latency Framework for Secure Neural Network Inference

16 January 2018

Papers citing "Gazelle: A Low Latency Framework for Secure Neural Network Inference"

11 / 311 papers shown

Title
nGraph-HE: A Graph Compiler for Deep Learning on Homomorphically Encrypted Data Fabian Boemer Yixing Lao Rosario Cammarota Casimir Wierzynski FedML 129 166 0 23 Oct 2018
Private Machine Learning in TensorFlow using Secure Computation Morten Dahl Jason V. Mancuso Yann Dupis Ben Decoste Morgan Giraud Ian Livingstone Justin Patriquin Gavin Uhma FedML 70 78 0 18 Oct 2018
Privado: Practical and Secure DNN Inference with Enclaves Karan Grover Shruti Tople Shweta Shinde Ranjita Bhagwan Ramachandran Ramjee FedML SILM 62 46 0 01 Oct 2018
Privacy-Preserving Deep Learning via Weight Transmission L. T. Phong T. Phuong FedML 69 87 0 10 Sep 2018
Secure Convolutional Neural Network using FHE Thomas Shortell A. Shokoufandeh FedML 13 2 0 11 Aug 2018
Security and Privacy Issues in Deep Learning Ho Bae Jaehee Jang Dahuin Jung Hyemi Jang Heonseok Ha Hyungyu Lee Sungroh Yoon SILM MIACV 140 79 0 31 Jul 2018
Efficient Deep Learning on Multi-Source Private Data Nicholas Hynes Raymond Cheng Basel Alomair FedML 76 102 0 17 Jul 2018
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service Amartya Sanyal Matt J. Kusner Adria Gascon Varun Kanade FedML 74 127 0 09 Jun 2018
Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware Florian Tramèr Dan Boneh FedML 184 401 0 08 Jun 2018
Blind Justice: Fairness with Encrypted Sensitive Attributes Niki Kilbertus Adria Gascon Matt J. Kusner Michael Veale Krishna P. Gummadi Adrian Weller 66 152 0 08 Jun 2018
Deep Private-Feature Extraction S. A. Ossia A. Taheri Ali Shahin Shamsabadi Kleomenis Katevas Hamed Haddadi Hamid R. Rabiee 54 96 0 09 Feb 2018