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
Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy Jiahao Xu Rui Hu Olivera Kotevska FedML 2 0 0 19 May 2025
Benchmarking Differentially Private Tabular Data Synthesis Kai Chen Xiaochen Li Chen Gong Ryan McKenna Tianhao Wang 28 0 0 18 Apr 2025
From Easy to Hard: Building a Shortcut for Differentially Private Image Synthesis Kecen Li Chen Gong Xiaochen Li Yuzhong Zhao Xinwen Hou Tianhao Wang 43 1 0 02 Apr 2025
Forward Learning with Differential Privacy Mingqian Feng Zeliang Zhang Jinyang Jiang Yijie Peng Chenliang Xu 47 0 0 01 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
AdvSGM: Differentially Private Graph Learning via Adversarial Skip-gram Model Sen Zhang Qingqing Ye Haibo Hu Jianliang Xu 42 0 0 27 Mar 2025
Bayesian Pseudo Posterior Mechanism for Differentially Private Machine Learning Robert Chew Matthew R. Williams Elan A. Segarra Alexander J. Preiss Amanda Konet T. Savitsky 41 0 0 27 Mar 2025
DPImageBench: A Unified Benchmark for Differentially Private Image Synthesis Chen Gong Kecen Li Zinan Lin Tianhao Wang 61 3 0 18 Mar 2025
An Optimization Framework for Differentially Private Sparse Fine-Tuning Mehdi Makni Kayhan Behdin Gabriel Afriat Zheng Xu Sergei Vassilvitskii Natalia Ponomareva Hussein Hazimeh Rahul Mazumder 56 0 0 17 Mar 2025
PREAMBLE: Private and Efficient Aggregation of Block Sparse Vectors and Applications Hilal Asi Vitaly Feldman Hannah Keller G. Rothblum Kunal Talwar FedML 59 1 0 14 Mar 2025
PRISM: Privacy-Preserving Improved Stochastic Masking for Federated Generative Models Kyeongkook Seo Dong-Jun Han Jaejun Yoo 45 0 0 11 Mar 2025
Controlled privacy leakage propagation throughout overlapping grouped learning Shahrzad Kiani Franziska Boenisch S. Draper FedML 72 0 0 06 Mar 2025
An Improved Privacy and Utility Analysis of Differentially Private SGD with Bounded Domain and Smooth Losses Hao Liang Feiyu Xiong Xinlei He Kaishun He Hong Xing 47 0 0 25 Feb 2025
Differentially Private Federated Learning With Time-Adaptive Privacy Spending Shahrzad Kiani Nupur Kulkarni Adam Dziedzic S. Draper Franziska Boenisch FedML Presented at ResearchTrend Connect \| FedML on 28 Mar 2025 149 0 0 25 Feb 2025
RAPID: Retrieval Augmented Training of Differentially Private Diffusion Models Tanqiu Jiang Changjiang Li Fenglong Ma Ting Wang 70 0 0 18 Feb 2025
Differential Privacy with Higher Utility by Exploiting Coordinate-wise Disparity: Laplace Mechanism Can Beat Gaussian in High Dimensions Gokularam Muthukrishnan Sheetal Kalyani 87 0 0 28 Jan 2025
Structure-Preference Enabled Graph Embedding Generation under Differential Privacy Sen Zhang Qingqing Ye Haibo Hu 49 0 0 08 Jan 2025
The Impact of Generalization Techniques on the Interplay Among Privacy, Utility, and Fairness in Image Classification Ahmad Hassanpour Amir Zarei Khawla Mallat Anderson Santana de Oliveira Bian Yang 81 0 0 16 Dec 2024
DP-2Stage: Adapting Language Models as Differentially Private Tabular Data Generators Tejumade Afonja Hui-Po Wang Raouf Kerkouche Mario Fritz SyDa 118 2 0 03 Dec 2024
NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA Marlon Tobaben Mohamed Ali Souibgui Rubèn Pérez Tito Khanh Nguyen Raouf Kerkouche ... Josep Lladós Ernest Valveny Antti Honkela Mario Fritz Dimosthenis Karatzas FedML 39 0 0 06 Nov 2024
Open LLMs are Necessary for Current Private Adaptations and Outperform their Closed Alternatives Vincent Hanke Tom Blanchard Franziska Boenisch Iyiola Emmanuel Olatunji Michael Backes Adam Dziedzic PILM 56 3 0 02 Nov 2024
Training Large ASR Encoders with Differential Privacy Geeticka Chauhan Steve Chien Om Thakkar Abhradeep Thakurta Arun Narayanan 33 1 0 21 Sep 2024
Differentially Private Stochastic Gradient Descent with Fixed-Size Minibatches: Tighter RDP Guarantees with or without Replacement Jeremiah Birrell Reza Ebrahimi R. Behnia Jason L. Pacheco 46 0 0 19 Aug 2024
Calibrating Noise for Group Privacy in Subsampled Mechanisms Yangfan Jiang Xinjian Luo Yin Yang Xiaokui Xiao 36 2 0 19 Aug 2024
Deep Learning with Data Privacy via Residual Perturbation Wenqi Tao Huaming Ling Zuoqiang Shi Bao Wang 21 2 0 11 Aug 2024
Weights Shuffling for Improving DPSGD in Transformer-based Models Jungang Yang Zhe Ji Liyao Xiang 43 0 0 22 Jul 2024
Universally Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergence Shuya Feng Meisam Mohammady Hanbin Hong Shenao Yan Ashish Kundu Binghui Wang Yuan Hong FedML 44 3 0 20 Jul 2024
Privacy of the last iterate in cyclically-sampled DP-SGD on nonconvex composite losses Weiwei Kong Mónica Ribero 37 3 0 07 Jul 2024
GCON: Differentially Private Graph Convolutional Network via Objective Perturbation Jianxin Wei Yizheng Zhu Xiaokui Xiao Ergute Bao Yin Yang Kuntai Cai Beng Chin Ooi AAML 29 0 0 06 Jul 2024
Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition C. Lebeda Matthew Regehr Gautam Kamath Thomas Steinke 53 9 0 27 May 2024
DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation Jie Xu Karthikeyan P. Saravanan Rogier van Dalen Haaris Mehmood David Tuckey Mete Ozay 56 6 0 10 May 2024
Improved Communication-Privacy Trade-offs in $L_2$ Mean Estimation under Streaming Differential Privacy Wei-Ning Chen Berivan Isik Peter Kairouz Albert No Sewoong Oh Zheng Xu 60 3 0 02 May 2024
Federated Learning and Differential Privacy Techniques on Multi-hospital Population-scale Electrocardiogram Data Vikhyat Agrawal Sunil Vasu Kalmady Venkataseetharam Manoj Malipeddi Manisimha Manthena Weijie Sun Saiful Islam Abram Hindle Padma Kaul Russell Greiner FedML 27 5 0 26 Apr 2024
Advances in Differential Privacy and Differentially Private Machine Learning Saswat Das Subhankar Mishra 30 4 0 06 Apr 2024
Privacy Backdoors: Stealing Data with Corrupted Pretrained Models Shanglun Feng Florian Tramèr SILM 40 14 0 30 Mar 2024
DP-RDM: Adapting Diffusion Models to Private Domains Without Fine-Tuning Jonathan Lebensold Maziar Sanjabi Pietro Astolfi Adriana Romero Soriano Kamalika Chaudhuri Mike Rabbat Chuan Guo DiffM 34 4 0 21 Mar 2024
Budget Recycling Differential Privacy Bo Jiang Jian Du Sagar Shamar Qiang Yan 26 1 0 18 Mar 2024
Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domains Lei Wang Jieming Bian Letian Zhang Chong Chen Jie Xu 37 7 0 14 Mar 2024
Privacy Amplification for the Gaussian Mechanism via Bounded Support Shengyuan Hu Saeed Mahloujifar Virginia Smith Kamalika Chaudhuri Chuan Guo FedML 44 1 0 07 Mar 2024
DPAdapter: Improving Differentially Private Deep Learning through Noise Tolerance Pre-training Zihao Wang Rui Zhu Dongruo Zhou Zhikun Zhang John C. Mitchell Haixu Tang Xiaofeng Wang AAML 45 6 0 05 Mar 2024
Differentially Private Representation Learning via Image Captioning Tom Sander Yaodong Yu Maziar Sanjabi Alain Durmus Yi Ma Kamalika Chaudhuri Chuan Guo 71 3 0 04 Mar 2024
State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey Chaoyu Zhang Shaoyu Li AILaw 57 3 0 25 Feb 2024
Closed-Form Bounds for DP-SGD against Record-level Inference Giovanni Cherubin Boris Köpf Andrew J. Paverd Shruti Tople Lukas Wutschitz Santiago Zanella Béguelin 46 2 0 22 Feb 2024
Revisiting Differentially Private Hyper-parameter Tuning Zihang Xiang Tianhao Wang Cheng-Long Wang Di Wang 34 6 0 20 Feb 2024
Momentum Approximation in Asynchronous Private Federated Learning Tao Yu Congzheng Song Jianyu Wang Mona Chitnis FedML 37 1 0 14 Feb 2024
Implicit Bias in Noisy-SGD: With Applications to Differentially Private Training Tom Sander Maxime Sylvestre Alain Durmus 31 1 0 13 Feb 2024
Subsampling is not Magic: Why Large Batch Sizes Work for Differentially Private Stochastic Optimisation Ossi Raisa Joonas Jälkö Antti Honkela 30 6 0 06 Feb 2024
Decentralised, Collaborative, and Privacy-preserving Machine Learning for Multi-Hospital Data Cong Fang Adam Dziedzic Lin Zhang Laura Oliva A. Verma Fahad Razak Nicolas Papernot Bo Wang OOD 17 11 0 31 Jan 2024
Cross-silo Federated Learning with Record-level Personalized Differential Privacy Junxu Liu Jian Lou Li Xiong Jinfei Liu Xiaofeng Meng 45 6 0 29 Jan 2024
Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners Rachel Redberg Antti Koskela Yu-Xiang Wang 70 5 0 31 Dec 2023