Subsampled Rényi Differential Privacy and Analytical Moments Accountant

31 July 2018

Papers citing "Subsampled Rényi Differential Privacy and Analytical Moments Accountant"

50 / 101 papers shown

Title
Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy Jiahao Xu Rui Hu Olivera Kotevska FedML 27 0 0 19 May 2025
PRISM: Privacy-Preserving Improved Stochastic Masking for Federated Generative Models Kyeongkook Seo Dong-Jun Han Jaejun Yoo 50 0 0 11 Mar 2025
Fed-SB: A Silver Bullet for Extreme Communication Efficiency and Performance in (Private) Federated LoRA Fine-Tuning Raghav Singhal Kaustubh Ponkshe Rohit Vartak Lav R. Varshney Praneeth Vepakomma FedML 84 1 0 24 Feb 2025
Differential Privacy with Higher Utility by Exploiting Coordinate-wise Disparity: Laplace Mechanism Can Beat Gaussian in High Dimensions Gokularam Muthukrishnan Sheetal Kalyani 89 0 0 28 Jan 2025
Structure-Preference Enabled Graph Embedding Generation under Differential Privacy Sen Zhang Qingqing Ye Haibo Hu 54 0 0 08 Jan 2025
Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning Fengyu Gao Ruida Zhou T. Wang Cong Shen Jing Yang 43 2 0 15 Oct 2024
DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction Xinwei Zhang Zhiqi Bu Borja Balle Mingyi Hong Meisam Razaviyayn Vahab Mirrokni 78 2 0 04 Oct 2024
Differentially Private Block-wise Gradient Shuffle for Deep Learning Zilong Zhang FedML 45 0 0 31 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 52 3 0 20 Jul 2024
Individualized Privacy Accounting via Subsampling with Applications in Combinatorial Optimization Badih Ghazi Pritish Kamath Ravi Kumar Pasin Manurangsi Adam Sealfon 55 0 0 28 May 2024
Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition C. Lebeda Matthew Regehr Gautam Kamath Thomas Steinke 58 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 61 6 0 10 May 2024
VFLGAN: Vertical Federated Learning-based Generative Adversarial Network for Vertically Partitioned Data Publication Xun Yuan Yang Yang P. Gope A. Pasikhani Biplab Sikdar 47 2 0 15 Apr 2024
Budget Recycling Differential Privacy Bo Jiang Jian Du Sagar Shamar Qiang Yan 34 1 0 18 Mar 2024
Closed-Form Bounds for DP-SGD against Record-level Inference Giovanni Cherubin Boris Köpf Andrew Paverd Shruti Tople Lukas Wutschitz Santiago Zanella Béguelin 51 2 0 22 Feb 2024
Cross-silo Federated Learning with Record-level Personalized Differential Privacy Junxu Liu Jian Lou Li Xiong Jinfei Liu Xiaofeng Meng 50 6 0 29 Jan 2024
Enhancing Trade-offs in Privacy, Utility, and Computational Efficiency through MUltistage Sampling Technique (MUST) Xingyuan Zhao Fang Liu 35 0 0 20 Dec 2023
Privacy Amplification by Iteration for ADMM with (Strongly) Convex Objective Functions T.-H. Hubert Chan Hao Xie Mengshi Zhao 51 1 0 14 Dec 2023
DP-NMT: Scalable Differentially-Private Machine Translation Timour Igamberdiev Doan Nam Long Vu Felix Künnecke Zhuo Yu Jannik Holmer Ivan Habernal 40 7 0 24 Nov 2023
Samplable Anonymous Aggregation for Private Federated Data Analysis Kunal Talwar Shan Wang Audra McMillan Vojta Jina Vitaly Feldman ... Congzheng Song Karl Tarbe Sebastian Vogt L. Winstrom Shundong Zhou FedML 43 13 0 27 Jul 2023
From Adaptive Query Release to Machine Unlearning Enayat Ullah R. Arora MU 30 4 0 20 Jul 2023
Population Expansion for Training Language Models with Private Federated Learning Tatsuki Koga Congzheng Song Martin Pelikan Mona Chitnis FedML 29 1 0 14 Jul 2023
Privacy Loss of Noisy Stochastic Gradient Descent Might Converge Even for Non-Convex Losses S. Asoodeh Mario Díaz 20 6 0 17 May 2023
PPGenCDR: A Stable and Robust Framework for Privacy-Preserving Cross-Domain Recommendation Xinting Liao Weiming Liu Xiaolin Zheng Binhui Yao Chaochao Chen 39 13 0 11 May 2023
DPAF: Image Synthesis via Differentially Private Aggregation in Forward Phase Chih-Hsun Lin Chia-Yi Hsu Chia-Mu Yu Yang Cao Chun-ying Huang 44 1 0 20 Apr 2023
PRIMO: Private Regression in Multiple Outcomes Seth Neel 25 0 0 07 Mar 2023
Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation Yilin Yang Kamil Adamczewski Danica J. Sutherland Xiaoxiao Li Mijung Park 38 14 0 03 Mar 2023
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy Natalia Ponomareva Hussein Hazimeh Alexey Kurakin Zheng Xu Carson E. Denison H. B. McMahan Sergei Vassilvitskii Steve Chien Abhradeep Thakurta 108 170 0 01 Mar 2023
Differentially Private Optimization for Smooth Nonconvex ERM Changyu Gao Stephen J. Wright 18 6 0 09 Feb 2023
Private GANs, Revisited Alex Bie Gautam Kamath Guojun Zhang 45 14 0 06 Feb 2023
Near Optimal Private and Robust Linear Regression Xiyang Liu Prateek Jain Weihao Kong Sewoong Oh A. Suggala 43 9 0 30 Jan 2023
Differentially Private Natural Language Models: Recent Advances and Future Directions Lijie Hu Ivan Habernal Lei Shen Di Wang AAML 37 19 0 22 Jan 2023
Training Differentially Private Graph Neural Networks with Random Walk Sampling Morgane Ayle Jan Schuchardt Lukas Gosch Daniel Zügner Stephan Günnemann FedML 34 6 0 02 Jan 2023
Lower Bounds for Rényi Differential Privacy in a Black-Box Setting T. Kutta Önder Askin Martin Dunsche 37 4 0 09 Dec 2022
A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization Ashwinee Panda Xinyu Tang Saeed Mahloujifar Vikash Sehwag Prateek Mittal 48 11 0 08 Dec 2022
Differentially Private Image Classification from Features Harsh Mehta Walid Krichene Abhradeep Thakurta Alexey Kurakin Ashok Cutkosky 67 7 0 24 Nov 2022
Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning Christopher A. Choquette-Choo H. B. McMahan Keith Rush Abhradeep Thakurta 44 42 0 12 Nov 2022
Private Set Generation with Discriminative Information Dingfan Chen Raouf Kerkouche Mario Fritz DD 35 36 0 07 Nov 2022
Local Model Reconstruction Attacks in Federated Learning and their Uses Ilias Driouich Chuan Xu Giovanni Neglia F. Giroire Eoin Thomas AAML FedML 41 2 0 28 Oct 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
An Ensemble Teacher-Student Learning Approach with Poisson Sub-sampling to Differential Privacy Preserving Speech Recognition Chao-Han Huck Yang Jun Qi Sabato Marco Siniscalchi Chin-Hui Lee 31 4 0 12 Oct 2022
Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies Zhanyu Wang Guang Cheng Jordan Awan 36 9 0 12 Oct 2022
CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning Samuel Maddock Alexandre Sablayrolles Pierre Stock FedML 33 22 0 06 Oct 2022
Federated Boosted Decision Trees with Differential Privacy Samuel Maddock Graham Cormode Tianhao Wang Carsten Maple S. Jha FedML 50 29 0 06 Oct 2022
Fine-Tuning with Differential Privacy Necessitates an Additional Hyperparameter Search Yannis Cattan Christopher A. Choquette-Choo Nicolas Papernot Abhradeep Thakurta 28 20 0 05 Oct 2022
Composition of Differential Privacy & Privacy Amplification by Subsampling Thomas Steinke 79 50 0 02 Oct 2022
Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks Chulin Xie Yunhui Long Pin-Yu Chen Qinbin Li Arash Nourian Sanmi Koyejo Bo Li FedML 68 13 0 08 Sep 2022
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM Chulin Xie Pin-Yu Chen Qinbin Li Arash Nourian Ce Zhang Bo Li FedML 47 16 0 20 Jul 2022
Libra: High-Utility Anonymization of Event Logs for Process Mining via Subsampling Gamal Elkoumy Marlon Dumas 25 6 0 27 Jun 2022
FLVoogd: Robust And Privacy Preserving Federated Learning Yuhang Tian Rui Wang Yan Qiao E. Panaousis K. Liang FedML 33 4 0 24 Jun 2022