Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations

Type: Preprint

Publication Date: 2018-01-01

Citations: 14

DOI: https://doi.org/10.48550/arxiv.1812.06825

Locations

  • arXiv (Cornell University) - View
  • DataCite API - View

Similar Works

Action Title Year Authors
+ Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations 2018 Di Wang
Adam Smith
Jinhui Xu
+ Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy 2020 Di Wang
Marco Gaboardi
Adam Smith
Jinhui Xu
+ PDF Chat Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy 2020 Di Wang
Marco Gaboardi
Adam Smith
Jinhui Xu
+ Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy 2020 Di Wang
Marco Gaboardi
Adam Smith
Jinhui Xu
+ Differentially Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds 2014 Raef Bassily
Adam Smith
Abhradeep Thakurta
+ Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses 2021 Andrew M. Lowy
Meisam Razaviyayn
+ PDF Chat Private Heterogeneous Federated Learning Without a Trusted Server Revisited: Error-Optimal and Communication-Efficient Algorithms for Convex Losses 2024 Changyu Gao
Andrew M. Lowy
Xingyu Zhou
Stephen J. Wright
+ Private Non-Convex Federated Learning Without a Trusted Server 2022 Andrew M. Lowy
Ali Ghafelebashi
Meisam Razaviyayn
+ Collect at Once, Use Effectively: Making Non-interactive Locally Private Learning Possible 2017 Kai Zheng
Wenlong Mou
Liwei Wang
+ Interaction is necessary for distributed learning with privacy or communication constraints 2019 Yuval Dagan
Vitaly Feldman
+ Interaction is necessary for distributed learning with privacy or communication constraints 2019 Yuval Dagan
Vitaly Feldman
+ Empirical Risk Minimization in Non-interactive Local Differential Privacy: Efficiency and High Dimensional Case 2018 Di Wang
Marco Gaboardi
Jinhui Xu
+ Oracle Efficient Private Non-Convex Optimization 2019 Seth Neel
Aaron Roth
Giuseppe Vietri
Zhiwei Steven Wu
+ Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses 2021 Andrew M. Lowy
Meisam Razaviyayn
+ Differentially Private Non-Convex Optimization under the KL Condition with Optimal Rates 2023 Michael Menart
Enayat Ullah
Raman Arora
Raef Bassily
Cristóbal Guzmán
+ Locally differentially private estimation of nonlinear functionals of discrete distributions 2021 Cristina Butucea
Yann Issartel
+ Locally differentially private estimation of nonlinear functionals of discrete distributions 2021 Cristina Butucea
Yann Issartel
+ Optimal Differentially Private Learning with Public Data 2023 Andrew M. Lowy
Zeman Li
Tianjian Huang
Meisam Razaviyayn
+ PDF Chat Private Convex Optimization via Exponential Mechanism 2024 Sivakanth Gopi
Yin Tat Lee
Daogao Liu
+ Privacy-preserving Prediction 2018 Cynthia Dwork
Vitaly Feldman

Works Cited by This (0)

Action Title Year Authors