QUOTIENT: Two-Party Secure Neural Network Training and Prediction

QUOTIENT: Two-Party Secure Neural Network Training and Prediction

8 July 2019

Ali Shahin Shamsabadi

Papers citing "QUOTIENT: Two-Party Secure Neural Network Training and Prediction"

18 / 18 papers shown

Title
A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy Yifei Zhang Dun Zeng Jinglong Luo Zenglin Xu Irwin King FedML 84 47 0 21 Feb 2023
SecSkyline: Fast Privacy-Preserving Skyline Queries over Encrypted Cloud Databases Yifeng Zheng Weibo Wang Songlei Wang Xiaohua Jia Hejiao Huang Cong Wang 16 7 0 15 Sep 2022
Adversarial Representation Sharing: A Quantitative and Secure Collaborative Learning Framework Jikun Chen Feng Qiang Na Ruan FedML 14 1 0 27 Mar 2022
Report: State of the Art Solutions for Privacy Preserving Machine Learning in the Medical Context J. Zalonis Frederik Armknecht Björn Grohmann Manuel Koch 30 4 0 27 Jan 2022
Optimizing Secure Decision Tree Inference Outsourcing Yifeng Zheng Cong Wang Ruochen Wang Huayi Duan Surya Nepal 11 6 0 31 Oct 2021
SEDML: Securely and Efficiently Harnessing Distributed Knowledge in Machine Learning Yansong Gao Qun Li Yifeng Zheng Guohong Wang Jiannan Wei Mang Su 32 3 0 26 Oct 2021
Trustworthy AI: From Principles to Practices Bo-wen Li Peng Qi Bo Liu Shuai Di Jingen Liu Jiquan Pei Jinfeng Yi Bowen Zhou 119 355 0 04 Oct 2021
MPC-Friendly Commitments for Publicly Verifiable Covert Security Nitin Agrawal James Bell Adria Gascon Matt J. Kusner 26 4 0 15 Sep 2021
Privacy-Preserving Machine Learning: Methods, Challenges and Directions Runhua Xu Nathalie Baracaldo J. Joshi 32 100 0 10 Aug 2021
Defending against Reconstruction Attack in Vertical Federated Learning Jiankai Sun Yuanshun Yao Weihao Gao Junyuan Xie Chong-Jun Wang AAML FedML 24 28 0 21 Jul 2021
Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputs Mohammad Malekzadeh Anastasia Borovykh Deniz Gündüz MIACV 14 42 0 25 May 2021
Privacy-preserving Decentralized Aggregation for Federated Learning Beomyeol Jeon S. Ferdous Muntasir Raihan Rahman A. Walid FedML 25 52 0 13 Dec 2020
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
POSEIDON: Privacy-Preserving Federated Neural Network Learning Sinem Sav Apostolos Pyrgelis J. Troncoso-Pastoriza D. Froelicher Jean-Philippe Bossuat João Sá Sousa Jean-Pierre Hubaux FedML 11 153 0 01 Sep 2020
VFL: A Verifiable Federated Learning with Privacy-Preserving for Big Data in Industrial IoT Anmin Fu Xianglong Zhang N. Xiong Yansong Gao Huaqun Wang FedML 16 174 0 27 Jul 2020
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing T. Ryffel Pierre Tholoniat D. Pointcheval Francis R. Bach FedML 28 94 0 08 Jun 2020
FALCON: Honest-Majority Maliciously Secure Framework for Private Deep Learning Sameer Wagh Shruti Tople Fabrice Benhamouda E. Kushilevitz Prateek Mittal T. Rabin FedML 33 295 0 05 Apr 2020
Learn to Forget: Machine Unlearning via Neuron Masking Yang Liu Zhuo Ma Ximeng Liu Jian-wei Liu Zhongyuan Jiang Jianfeng Ma Philip Yu K. Ren MU 20 61 0 24 Mar 2020