OLIVE: Oblivious Federated Learning on Trusted Execution Environment
against the risk of sparsification

OLIVE: Oblivious Federated Learning on Trusted Execution Environment against the risk of sparsification

15 February 2022

Yang Cao

Masatoshi Yoshikawa

Papers citing "OLIVE: Oblivious Federated Learning on Trusted Execution Environment against the risk of sparsification"

5 / 5 papers shown

Title
When the Curious Abandon Honesty: Federated Learning Is Not Private Franziska Boenisch Adam Dziedzic R. Schuster Ali Shahin Shamsabadi Ilia Shumailov Nicolas Papernot FedML AAML 64 180 0 06 Dec 2021
The Privacy-preserving Padding Problem: Non-negative Mechanisms for Conservative Answers with Differential Privacy Benjamin M. Case James Honaker Mahnush Movahedi 9 3 0 15 Oct 2021
Citadel: Protecting Data Privacy and Model Confidentiality for Collaborative Learning with SGX Chengliang Zhang Junzhe Xia Baichen Yang Huancheng Puyang W. Wang Ruichuan Chen Istemi Ekin Akkus Paarijaat Aditya Feng Yan FedML 51 39 0 04 May 2021
Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications Matthias Paulik M. Seigel Henry Mason Dominic Telaar Joris Kluivers ... Dominic Hughes O. Javidbakht Fei Dong Rehan Rishi Stanley Hung FedML 175 124 0 16 Feb 2021
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Kunal Talwar Abhradeep Thakurta 134 420 0 29 Nov 2018