Rényi Differential Privacy of the Sampled Gaussian Mechanism

28 August 2019

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

50 / 177 papers shown

Title
Privacy-Preserving Models for Legal Natural Language Processing Ying Yin Ivan Habernal PILM AILaw 11 8 0 05 Nov 2022
Differentially Private Diffusion Models Tim Dockhorn Tianshi Cao Arash Vahdat Karsten Kreis DiffM 32 91 0 18 Oct 2022
DPIS: An Enhanced Mechanism for Differentially Private SGD with Importance Sampling Jianxin Wei Ergute Bao X. Xiao Yifan Yang 46 20 0 18 Oct 2022
Differentially Private Deep Learning with ModelMix Hanshen Xiao Jun Wan S. Devadas 29 3 0 07 Oct 2022
TAN Without a Burn: Scaling Laws of DP-SGD Tom Sander Pierre Stock Alexandre Sablayrolles FedML 47 42 0 07 Oct 2022
CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning Samuel Maddock Alexandre Sablayrolles Pierre Stock FedML 20 22 0 06 Oct 2022
Composition of Differential Privacy & Privacy Amplification by Subsampling Thomas Steinke 69 50 0 02 Oct 2022
Differentially Private Bias-Term Fine-tuning of Foundation Models Zhiqi Bu Yu-Xiang Wang Sheng Zha George Karypis 28 46 0 30 Sep 2022
Individual Privacy Accounting with Gaussian Differential Privacy A. Koskela Marlon Tobaben Antti Honkela 42 18 0 30 Sep 2022
On the Choice of Databases in Differential Privacy Composition Valentin Hartmann Vincent Bindschaedler Robert West 34 0 0 27 Sep 2022
Privacy-Preserving Online Content Moderation: A Federated Learning Use Case Pantelitsa Leonidou N. Kourtellis Nikos Salamanos Michael Sirivianos 13 1 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 29 14 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 32 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 25 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 24 22 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 15 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 16 3 0 05 Jul 2022
High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent Paul Mangold A. Bellet Joseph Salmon Marc Tommasi 51 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 8 38 0 27 Jun 2022
On Privacy and Personalization in Cross-Silo Federated Learning Ziyu Liu Shengyuan Hu Zhiwei Steven Wu Virginia Smith FedML 22 53 0 16 Jun 2022
Disparate Impact in Differential Privacy from Gradient Misalignment Maria S. Esipova Atiyeh Ashari Ghomi Yaqiao Luo Jesse C. Cresswell 29 25 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 21 18 0 06 Jun 2022
Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss Jason M. Altschuler Kunal Talwar FedML 36 57 0 27 May 2022
Large Scale Transfer Learning for Differentially Private Image Classification Harsh Mehta Abhradeep Thakurta Alexey Kurakin Ashok Cutkosky 17 39 0 06 May 2022
Privacy Amplification via Random Participation in Federated Learning Burak Hasircioglu Deniz Gunduz FedML 27 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 35 218 0 28 Apr 2022
Differentially Private Learning Needs Hidden State (Or Much Faster Convergence) Jiayuan Ye Reza Shokri FedML 35 44 0 10 Mar 2022
Defending against Reconstruction Attacks with Rényi Differential Privacy Pierre Stock I. Shilov Ilya Mironov Alexandre Sablayrolles AAML SILM MIACV 28 39 0 15 Feb 2022
Private Adaptive Optimization with Side Information Tian Li Manzil Zaheer Sashank J. Reddi Virginia Smith 37 35 0 12 Feb 2022
Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Data Shadi Rahimian Raouf Kerkouche I. Kurth Mario Fritz FedML 22 11 0 08 Feb 2022
Bounding Training Data Reconstruction in Private (Deep) Learning Chuan Guo Brian Karrer Kamalika Chaudhuri L. V. D. van der Maaten 115 53 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 23 4 0 18 Jan 2022
Reconstructing Training Data with Informed Adversaries Borja Balle Giovanni Cherubin Jamie Hayes MIACV AAML 45 158 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 21 1 0 14 Dec 2021
Improving Differentially Private SGD via Randomly Sparsified Gradients Junyi Zhu Matthew B. Blaschko 30 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 21 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 19 93 0 22 Nov 2021
On the Importance of Difficulty Calibration in Membership Inference Attacks Lauren Watson Chuan Guo Graham Cormode Alex Sablayrolles 31 119 0 15 Nov 2021
DP-REC: Private & Communication-Efficient Federated Learning Aleksei Triastcyn M. Reisser Christos Louizos FedML 24 16 0 09 Nov 2021
Differentially Private Coordinate Descent for Composite Empirical Risk Minimization Paul Mangold A. Bellet Joseph Salmon Marc Tommasi 32 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 39 27 0 16 Oct 2021
Adaptive Differentially Private Empirical Risk Minimization Xiaoxia Wu Lingxiao Wang Irina Cristali Quanquan Gu Rebecca Willett 38 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 59 25 0 12 Oct 2021
The Skellam Mechanism for Differentially Private Federated Learning Naman Agarwal Peter Kairouz Ziyu Liu FedML 22 122 0 11 Oct 2021
Hyperparameter Tuning with Renyi Differential Privacy Nicolas Papernot Thomas Steinke 135 120 0 07 Oct 2021
Opacus: User-Friendly Differential Privacy Library in PyTorch Ashkan Yousefpour I. Shilov Alexandre Sablayrolles Davide Testuggine Karthik Prasad ... Sayan Gosh Akash Bharadwaj Jessica Zhao Graham Cormode Ilya Mironov VLM 168 350 0 25 Sep 2021
A unified interpretation of the Gaussian mechanism for differential privacy through the sensitivity index Georgios Kaissis Moritz Knolle F. Jungmann Alexander Ziller Dmitrii Usynin Daniel Rueckert 22 1 0 22 Sep 2021
Renyi Differential Privacy of the Subsampled Shuffle Model in Distributed Learning Antonious M. Girgis Deepesh Data Suhas Diggavi FedML 30 21 0 19 Jul 2021
Differentially Private Bayesian Neural Networks on Accuracy, Privacy and Reliability Qiyiwen Zhang Zhiqi Bu Kan Chen Qi Long BDL UQCV 19 11 0 18 Jul 2021
Differentially private federated deep learning for multi-site medical image segmentation Alexander Ziller Dmitrii Usynin Nicolas W. Remerscheid Moritz Knolle Marcus R. Makowski R. Braren Daniel Rueckert Georgios Kaissis FedML 6 22 0 06 Jul 2021