Rényi Differential Privacy of the Sampled Gaussian Mechanism

28 August 2019

Papers citing "Rényi Differential Privacy of the Sampled Gaussian Mechanism"

50 / 182 papers shown

Title
Differentially Private Adaptive Optimization with Delayed Preconditioners Tian Li Manzil Zaheer Ziyu Liu Sashank J. Reddi H. B. McMahan Virginia Smith 104 11 0 01 Dec 2022
Adap DP-FL: Differentially Private Federated Learning with Adaptive Noise Jie Fu Zhili Chen Xiao Han FedML 64 31 0 29 Nov 2022
Differentially Private Image Classification from Features Harsh Mehta Walid Krichene Abhradeep Thakurta Alexey Kurakin Ashok Cutkosky 113 8 0 24 Nov 2022
SA-DPSGD: Differentially Private Stochastic Gradient Descent based on Simulated Annealing Jie Fu Zhili Chen Xinpeng Ling 81 1 0 14 Nov 2022
Private Set Generation with Discriminative Information Dingfan Chen Raouf Kerkouche Mario Fritz DD 80 39 0 07 Nov 2022
Privacy-Preserving Models for Legal Natural Language Processing Ying Yin Ivan Habernal PILM AILaw 74 8 0 05 Nov 2022
Differentially Private Diffusion Models Tim Dockhorn Tianshi Cao Arash Vahdat Karsten Kreis DiffM 89 100 0 18 Oct 2022
DPIS: An Enhanced Mechanism for Differentially Private SGD with Importance Sampling Jianxin Wei Ergute Bao X. Xiao Yifan Yang 90 21 0 18 Oct 2022
Differentially Private Deep Learning with ModelMix Hanshen Xiao Jun Wan S. Devadas 75 3 0 07 Oct 2022
TAN Without a Burn: Scaling Laws of DP-SGD Tom Sander Pierre Stock Alexandre Sablayrolles FedML 86 43 0 07 Oct 2022
CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning Samuel Maddock Alexandre Sablayrolles Pierre Stock FedML 139 23 0 06 Oct 2022
Composition of Differential Privacy & Privacy Amplification by Subsampling Thomas Steinke 166 54 0 02 Oct 2022
Differentially Private Bias-Term Fine-tuning of Foundation Models Zhiqi Bu Yu Wang Sheng Zha George Karypis 126 48 0 30 Sep 2022
Individual Privacy Accounting with Gaussian Differential Privacy A. Koskela Marlon Tobaben Antti Honkela 107 20 0 30 Sep 2022
On the Choice of Databases in Differential Privacy Composition Valentin Hartmann Vincent Bindschaedler Robert West 53 0 0 27 Sep 2022
Privacy-Preserving Online Content Moderation: A Federated Learning Use Case Pantelitsa Leonidou N. Kourtellis Nikos Salamanos Michael Sirivianos 33 2 0 23 Sep 2022
Distribution inference risks: Identifying and mitigating sources of leakage Valentin Hartmann Léo Meynent Maxime Peyrard Dimitrios Dimitriadis Shruti Tople Robert West MIACV 63 16 0 18 Sep 2022
Renyi Differential Privacy of Propose-Test-Release and Applications to Private and Robust Machine Learning Jiachen T. Wang Saeed Mahloujifar Shouda Wang R. Jia Prateek Mittal AAML 82 5 0 16 Sep 2022
The Saddle-Point Accountant for Differential Privacy Wael Alghamdi S. Asoodeh Flavio du Pin Calmon Juan Felipe Gomez O. Kosut Lalitha Sankar Fei Wei 70 7 0 20 Aug 2022
Widespread Underestimation of Sensitivity in Differentially Private Libraries and How to Fix It Sílvia Casacuberta Michael Shoemate Salil P. Vadhan Connor Wagaman 94 25 0 21 Jul 2022
Training Large-Vocabulary Neural Language Models by Private Federated Learning for Resource-Constrained Devices Mingbin Xu Congzheng Song Ye Tian Neha Agrawal Filip Granqvist ... Shiyi Han Yaqiao Deng Leo Liu Anmol Walia Alex Jin FedML 89 22 0 18 Jul 2022
A Generative Framework for Personalized Learning and Estimation: Theory, Algorithms, and Privacy Kaan Ozkara Antonious M. Girgis Deepesh Data Suhas Diggavi FedML 63 3 0 05 Jul 2022
High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent Paul Mangold A. Bellet Joseph Salmon Marc Tommasi 80 5 0 04 Jul 2022
Normalized/Clipped SGD with Perturbation for Differentially Private Non-Convex Optimization Xiaodong Yang Huishuai Zhang Wei Chen Tie-Yan Liu 83 38 0 27 Jun 2022
On Privacy and Personalization in Cross-Silo Federated Learning Ziyu Liu Shengyuan Hu Zhiwei Steven Wu Virginia Smith FedML 113 56 0 16 Jun 2022
Disparate Impact in Differential Privacy from Gradient Misalignment Maria S. Esipova Atiyeh Ashari Ghomi Yaqiao Luo Jesse C. Cresswell 104 30 0 15 Jun 2022
Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent Da Yu Gautam Kamath Janardhan Kulkarni Tie-Yan Liu Jian Yin Huishuai Zhang 156 22 0 06 Jun 2022
Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss Jason M. Altschuler Kunal Talwar FedML 146 61 0 27 May 2022
Large Scale Transfer Learning for Differentially Private Image Classification Harsh Mehta Abhradeep Thakurta Alexey Kurakin Ashok Cutkosky 85 41 0 06 May 2022
Privacy Amplification via Random Participation in Federated Learning Burak Hasircioglu Deniz Gunduz FedML 34 1 0 03 May 2022
Unlocking High-Accuracy Differentially Private Image Classification through Scale Soham De Leonard Berrada Jamie Hayes Samuel L. Smith Borja Balle 97 233 0 28 Apr 2022
Differentially Private Learning Needs Hidden State (Or Much Faster Convergence) Jiayuan Ye Reza Shokri FedML 107 47 0 10 Mar 2022
Defending against Reconstruction Attacks with Rényi Differential Privacy Pierre Stock I. Shilov Ilya Mironov Alexandre Sablayrolles AAML SILM MIACV 65 40 0 15 Feb 2022
Private Adaptive Optimization with Side Information Tian Li Manzil Zaheer Sashank J. Reddi Virginia Smith 95 36 0 12 Feb 2022
Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Data Shadi Rahimian Raouf Kerkouche I. Kurth Mario Fritz FedML 70 11 0 08 Feb 2022
Bounding Training Data Reconstruction in Private (Deep) Learning Chuan Guo Brian Karrer Kamalika Chaudhuri Laurens van der Maaten 174 55 0 28 Jan 2022
A Privacy-Preserving Unsupervised Domain Adaptation Framework for Clinical Text Analysis Qi A. An Ruijiang Li Lin Gu Hao Zhang Qingyu Chen Zhiyong Lu Fei Wang Yingying Zhu OOD 36 4 0 18 Jan 2022
Reconstructing Training Data with Informed Adversaries Borja Balle Giovanni Cherubin Jamie Hayes MIACV AAML 95 171 0 13 Jan 2022
Generalization Bounds for Stochastic Gradient Langevin Dynamics: A Unified View via Information Leakage Analysis Bingzhe Wu Zhicong Liang Yatao Bian Chaochao Chen Junzhou Huang Yuan Yao 49 1 0 14 Dec 2021
Improving Differentially Private SGD via Randomly Sparsified Gradients Junyi Zhu Matthew B. Blaschko 78 5 0 01 Dec 2021
Evaluating Privacy-Preserving Machine Learning in Critical Infrastructures: A Case Study on Time-Series Classification Dominique Mercier Adriano Lucieri Mohsin Munir Andreas Dengel Sheraz Ahmed 64 11 0 29 Nov 2021
Decentralized Federated Learning through Proxy Model Sharing Shivam Kalra Junfeng Wen Jesse C. Cresswell M. Volkovs Hamid R. Tizhoosh FedML 99 100 0 22 Nov 2021
On the Importance of Difficulty Calibration in Membership Inference Attacks Lauren Watson Chuan Guo Graham Cormode Alex Sablayrolles 111 135 0 15 Nov 2021
DP-REC: Private & Communication-Efficient Federated Learning Aleksei Triastcyn M. Reisser Christos Louizos FedML 57 16 0 09 Nov 2021
Differentially Private Coordinate Descent for Composite Empirical Risk Minimization Paul Mangold A. Bellet Joseph Salmon Marc Tommasi 109 14 0 22 Oct 2021
DPNAS: Neural Architecture Search for Deep Learning with Differential Privacy Anda Cheng Jiaxing Wang Xi Sheryl Zhang Qiang Chen Peisong Wang Jian Cheng 83 29 0 16 Oct 2021
Adaptive Differentially Private Empirical Risk Minimization Xiaoxia Wu Lingxiao Wang Irina Cristali Quanquan Gu Rebecca Willett 158 6 0 14 Oct 2021
Not all noise is accounted equally: How differentially private learning benefits from large sampling rates Friedrich Dörmann Osvald Frisk L. Andersen Christian Fischer Pedersen FedML 104 25 0 12 Oct 2021
The Skellam Mechanism for Differentially Private Federated Learning Naman Agarwal Peter Kairouz Ziyu Liu FedML 88 127 0 11 Oct 2021
Hyperparameter Tuning with Renyi Differential Privacy Nicolas Papernot Thomas Steinke 194 129 0 07 Oct 2021