Concentrated Differentially Private Gradient Descent with Adaptive per-Iteration Privacy Budget

28 August 2018

Papers citing "Concentrated Differentially Private Gradient Descent with Adaptive per-Iteration Privacy Budget"

19 / 19 papers shown

Title
Layered Randomized Quantization for Communication-Efficient and Privacy-Preserving Distributed Learning Guangfeng Yan Tan Li Tian-Shing Lan Kui Wu Linqi Song 32 6 0 12 Dec 2023
DP-SGD with weight clipping Antoine Barczewski Jan Ramon 18 1 0 27 Oct 2023
Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance Xin Gu Gautam Kamath Zhiwei Steven Wu 33 12 0 02 Mar 2023
Adap DP-FL: Differentially Private Federated Learning with Adaptive Noise Jie Fu Zhili Chen Xiao Han FedML 32 28 0 29 Nov 2022
SA-DPSGD: Differentially Private Stochastic Gradient Descent based on Simulated Annealing Jie Fu Zhili Chen Xinpeng Ling 37 0 0 14 Nov 2022
DPIS: An Enhanced Mechanism for Differentially Private SGD with Importance Sampling Jianxin Wei Ergute Bao X. Xiao Yifan Yang 48 20 0 18 Oct 2022
Encoded Gradients Aggregation against Gradient Leakage in Federated Learning Dun Zeng Shiyu Liu Siqi Liang Zonghang Li Hongya Wang Irwin King Zenglin Xu FedML 34 0 0 26 May 2022
DPNAS: Neural Architecture Search for Deep Learning with Differential Privacy Anda Cheng Jiaxing Wang Xi Sheryl Zhang Qiang Chen Peisong Wang Jian Cheng 39 27 0 16 Oct 2021
Adaptive Differentially Private Empirical Risk Minimization Xiaoxia Wu Lingxiao Wang Irina Cristali Quanquan Gu Rebecca Willett 45 6 0 14 Oct 2021
Efficient Hyperparameter Optimization for Differentially Private Deep Learning Aman Priyanshu Rakshit Naidu Fatemehsadat Mireshghallah Mohammad Malekzadeh 44 5 0 09 Aug 2021
Survey: Leakage and Privacy at Inference Time Marija Jegorova Chaitanya Kaul Charlie Mayor Alison Q. OÑeil Alexander Weir Roderick Murray-Smith Sotirios A. Tsaftaris PILM MIACV 33 71 0 04 Jul 2021
Differentially private inference via noisy optimization Marco Avella-Medina Casey Bradshaw Po-Ling Loh FedML 45 29 0 19 Mar 2021
Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning Da Yu Huishuai Zhang Wei Chen Tie-Yan Liu FedML SILM 94 111 0 25 Feb 2021
Stochastic Adaptive Line Search for Differentially Private Optimization Chen Chen Jaewoo Lee 30 14 0 18 Aug 2020
User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization Kang Wei Jun Li Ming Ding Chuan Ma Hang Su Bo Zhang H. Vincent Poor FedML 25 11 0 29 Feb 2020
An Adaptive and Fast Convergent Approach to Differentially Private Deep Learning Zhiying Xu Shuyu Shi A. Liu Jun Zhao Lin Chen FedML 47 36 0 19 Dec 2019
DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM Bao Wang Quanquan Gu M. Boedihardjo Farzin Barekat Stanley J. Osher 24 25 0 28 Jun 2019
SoK: Differential Privacies Damien Desfontaines Balázs Pejó 38 122 0 04 Jun 2019
A Hybrid Approach to Privacy-Preserving Federated Learning Stacey Truex Nathalie Baracaldo Ali Anwar Thomas Steinke Heiko Ludwig Rui Zhang Yi Zhou FedML 31 884 0 07 Dec 2018