Hyperparameter Tuning with Renyi Differential Privacy

7 October 2021

Papers citing "Hyperparameter Tuning with Renyi Differential Privacy"

28 / 28 papers shown

Title
DPolicy: Managing Privacy Risks Across Multiple Releases with Differential Privacy Nicolas Küchler Alexander Viand Hidde Lycklama Anwar Hithnawi 26 0 0 10 May 2025
The Importance of Being Discrete: Measuring the Impact of Discretization in End-to-End Differentially Private Synthetic Data Georgi Ganev Meenatchi Sundaram Muthu Selva Annamalai Sofiane Mahiou Emiliano De Cristofaro 24 2 0 09 Apr 2025
DC-SGD: Differentially Private SGD with Dynamic Clipping through Gradient Norm Distribution Estimation Chengkun Wei Weixian Li Chen Gong Wenzhi Chen 58 0 0 29 Mar 2025
Better Locally Private Sparse Estimation Given Multiple Samples Per User Yuheng Ma Ke Jia Hanfang Yang FedML 36 1 0 08 Aug 2024
DPDR: Gradient Decomposition and Reconstruction for Differentially Private Deep Learning Yixuan Liu Li Xiong Yuhan Liu Yujie Gu Ruixuan Liu Hong Chen 38 1 0 04 Jun 2024
Delving into Differentially Private Transformer Youlong Ding Xueyang Wu Yining Meng Yonggang Luo Hao Wang Weike Pan 29 5 0 28 May 2024
Private Count Release: A Simple and Scalable Approach for Private Data Analytics Ryan Rogers 29 0 0 08 Mar 2024
Blink: Link Local Differential Privacy in Graph Neural Networks via Bayesian Estimation Xiaochen Zhu Vincent Y. F. Tan Xiaokui Xiao 22 9 0 06 Sep 2023
On the Fairness Impacts of Private Ensembles Models Cuong Tran Ferdinando Fioretto 39 4 0 19 May 2023
Certified private data release for sparse Lipschitz functions Konstantin Donhauser J. Lokna Amartya Sanyal M. Boedihardjo R. Honig Fanny Yang 28 3 0 19 Feb 2023
Privately Customizing Prefinetuning to Better Match User Data in Federated Learning Charlie Hou Hongyuan Zhan Akshat Shrivastava Sida I. Wang S. Livshits Giulia Fanti Daniel Lazar FedML 29 15 0 17 Feb 2023
An Empirical Analysis of Fairness Notions under Differential Privacy Anderson Santana de Oliveira Caelin Kaplan Khawla Mallat Tanmay Chakraborty FedML 13 7 0 06 Feb 2023
A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization Ashwinee Panda Xinyu Tang Saeed Mahloujifar Vikash Sehwag Prateek Mittal 36 11 0 08 Dec 2022
Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy Ergute Bao Yizheng Zhu X. Xiao Y. Yang Beng Chin Ooi B. Tan Khin Mi Mi Aung FedML 28 18 0 08 Dec 2022
Private Multi-Winner Voting for Machine Learning Adam Dziedzic Christopher A. Choquette-Choo Natalie Dullerud Vinith M. Suriyakumar Ali Shahin Shamsabadi Muhammad Ahmad Kaleem S. Jha Nicolas Papernot Xiao Wang 31 1 0 23 Nov 2022
Learning to Generate Image Embeddings with User-level Differential Privacy Zheng Xu Maxwell D. Collins Yuxiao Wang Liviu Panait Sewoong Oh S. Augenstein Ting Liu Florian Schroff H. B. McMahan FedML 30 29 0 20 Nov 2022
Revisiting Hyperparameter Tuning with Differential Privacy Youlong Ding Xueyang Wu 16 0 0 03 Nov 2022
DPIS: An Enhanced Mechanism for Differentially Private SGD with Importance Sampling Jianxin Wei Ergute Bao X. Xiao Y. Yang 39 20 0 18 Oct 2022
Fine-Tuning with Differential Privacy Necessitates an Additional Hyperparameter Search Yannis Cattan Christopher A. Choquette-Choo Nicolas Papernot Abhradeep Thakurta 15 20 0 05 Oct 2022
Algorithms that Approximate Data Removal: New Results and Limitations Vinith M. Suriyakumar Ashia C. Wilson MU 40 27 0 25 Sep 2022
FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning Yuanhao Xiong Ruochen Wang Minhao Cheng Felix X. Yu Cho-Jui Hsieh FedML DD 36 82 0 20 Jul 2022
Beyond Uniform Lipschitz Condition in Differentially Private Optimization Rudrajit Das Satyen Kale Zheng Xu Tong Zhang Sujay Sanghavi 22 17 0 21 Jun 2022
On Privacy and Personalization in Cross-Silo Federated Learning Ziyu Liu Shengyuan Hu Zhiwei Steven Wu Virginia Smith FedML 20 51 0 16 Jun 2022
Toward Training at ImageNet Scale with Differential Privacy Alexey Kurakin Shuang Song Steve Chien Roxana Geambasu Andreas Terzis Abhradeep Thakurta 30 99 0 28 Jan 2022
Plume: Differential Privacy at Scale Kareem Amin Jennifer Gillenwater Matthew Joseph Alex Kulesza Sergei Vassilvitskii 15 9 0 27 Jan 2022
The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection Shubhankar Mohapatra Sajin Sasy Xi He Gautam Kamath Om Thakkar 109 32 0 09 Nov 2021
Extracting Training Data from Large Language Models Nicholas Carlini Florian Tramèr Eric Wallace Matthew Jagielski Ariel Herbert-Voss ... Tom B. Brown D. Song Ulfar Erlingsson Alina Oprea Colin Raffel MLAU SILM 290 1,814 0 14 Dec 2020
Tempered Sigmoid Activations for Deep Learning with Differential Privacy Nicolas Papernot Abhradeep Thakurta Shuang Song Steve Chien Ulfar Erlingsson AAML 139 178 0 28 Jul 2020