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Numerical Composition of Differential Privacy

5 June 2021

Papers citing "Numerical Composition of Differential Privacy"

50 / 131 papers shown

Title
Subsampling is not Magic: Why Large Batch Sizes Work for Differentially Private Stochastic Optimisation Ossi Raisa Hibiki Ito Antti Honkela 77 6 0 06 Feb 2024
Tight Group-Level DP Guarantees for DP-SGD with Sampling via Mixture of Gaussians Mechanisms Arun Ganesh 75 3 0 17 Jan 2024
Private Fine-tuning of Large Language Models with Zeroth-order Optimization Xinyu Tang Ashwinee Panda Milad Nasr Saeed Mahloujifar Prateek Mittal 218 26 0 09 Jan 2024
Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners Rachel Redberg Antti Koskela Yu-Xiang Wang 139 6 0 31 Dec 2023
Privacy-Aware Document Visual Question Answering Rubèn Pérez Tito Khanh Nguyen Marlon Tobaben Raouf Kerkouche Mohamed Ali Souibgui ... Lei Kang Ernest Valveny Antti Honkela Mario Fritz Dimosthenis Karatzas 73 13 0 15 Dec 2023
Learn to Unlearn for Deep Neural Networks: Minimizing Unlearning Interference with Gradient Projection Tuan Hoang Santu Rana Sunil R. Gupta Svetha Venkatesh BDL MU 79 21 0 07 Dec 2023
Zero redundancy distributed learning with differential privacy Zhiqi Bu Justin Chiu Ruixuan Liu Sheng Zha George Karypis 90 8 0 20 Nov 2023
Are Normalizing Flows the Key to Unlocking the Exponential Mechanism? Robert A. Bridges Vandy J. Tombs Christopher B. Stanley 38 1 0 15 Nov 2023
Sparsity-Preserving Differentially Private Training of Large Embedding Models Badih Ghazi Yangsibo Huang Pritish Kamath Ravi Kumar Pasin Manurangsi Amer Sinha Chiyuan Zhang 61 2 0 14 Nov 2023
On the accuracy and efficiency of group-wise clipping in differentially private optimization Zhiqi Bu Ruixuan Liu Yu Wang Sheng Zha George Karypis VLM 66 4 0 30 Oct 2023
Privately Aligning Language Models with Reinforcement Learning Fan Wu Huseyin A. Inan A. Backurs Varun Chandrasekaran Janardhan Kulkarni Robert Sim 92 7 0 25 Oct 2023
Differentially Private Data Generation with Missing Data Shubhankar Mohapatra Jianqiao Zong F. Kerschbaum Xi He SyDa 70 1 0 17 Oct 2023
PrIeD-KIE: Towards Privacy Preserved Document Key Information Extraction S. Saifullah S. Agne Andreas Dengel Sheraz Ahmed 71 0 0 05 Oct 2023
Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation Xinyu Tang Richard Shin Huseyin A. Inan Andre Manoel Fatemehsadat Mireshghallah Zinan Lin Sivakanth Gopi Janardhan Kulkarni Robert Sim 120 60 0 21 Sep 2023
DP-Forward: Fine-tuning and Inference on Language Models with Differential Privacy in Forward Pass Minxin Du Xiang Yue Sherman S. M. Chow Tianhao Wang Chenyu Huang Huan Sun SILM 107 65 0 13 Sep 2023
Threshold KNN-Shapley: A Linear-Time and Privacy-Friendly Approach to Data Valuation Jiachen T. Wang Yuqing Zhu Yu Wang R. Jia Prateek Mittal TDI 107 12 0 30 Aug 2023
Client-Level Differential Privacy via Adaptive Intermediary in Federated Medical Imaging Meirui Jiang Yuan Zhong Anjie Le Xiaoxiao Li Qianming Dou FedML 93 5 0 24 Jul 2023
Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy Georgios Kaissis Jamie Hayes Alexander Ziller Daniel Rueckert AAML 81 13 0 08 Jul 2023
Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD Anvith Thudi Hengrui Jia Casey Meehan Ilia Shumailov Nicolas Papernot 117 7 0 01 Jul 2023
Differentially Private Image Classification by Learning Priors from Random Processes Xinyu Tang Ashwinee Panda Vikash Sehwag Prateek Mittal 89 21 0 08 Jun 2023
PILLAR: How to make semi-private learning more effective Francesco Pinto Yaxian Hu Fanny Yang Amartya Sanyal 99 12 0 06 Jun 2023
Jointly Reparametrized Multi-Layer Adaptation for Efficient and Private Tuning Umang Gupta Aram Galstyan Greg Ver Steeg 53 2 0 30 May 2023
Personalized DP-SGD using Sampling Mechanisms Geon Heo Junseok Seo Steven Euijong Whang 60 2 0 24 May 2023
Differentially Private Synthetic Data via Foundation Model APIs 1: Images Zinan Lin Sivakanth Gopi Janardhan Kulkarni Harsha Nori Sergey Yekhanin 163 44 0 24 May 2023
Selective Pre-training for Private Fine-tuning Da Yu Sivakanth Gopi Janardhan Kulkarni Zinan Lin Saurabh Naik Tomasz Religa Jian Yin Huishuai Zhang 94 19 0 23 May 2023
Privacy Auditing with One (1) Training Run Thomas Steinke Milad Nasr Matthew Jagielski 118 87 0 15 May 2023
Privacy-Preserving In-Context Learning for Large Language Models Tong Wu Ashwinee Panda Jiachen T. Wang Prateek Mittal 114 32 0 02 May 2023
SAFE: Machine Unlearning With Shard Graphs Yonatan Dukler Benjamin Bowman Alessandro Achille Aditya Golatkar A. Swaminathan Stefano Soatto MU 87 25 0 25 Apr 2023
A Randomized Approach for Tight Privacy Accounting Jiachen T. Wang Saeed Mahloujifar Tong Wu R. Jia Prateek Mittal 106 10 0 17 Apr 2023
Practical Differentially Private and Byzantine-resilient Federated Learning Zihang Xiang Tianhao Wang Wanyu Lin Di Wang FedML 73 23 0 15 Apr 2023
Privacy Amplification via Shuffling: Unified, Simplified, and Tightened Shaowei Wang FedML 107 9 0 11 Apr 2023
Arbitrary Decisions are a Hidden Cost of Differentially Private Training B. Kulynych Hsiang Hsu Carmela Troncoso Flavio du Pin Calmon 103 18 0 28 Feb 2023
Bounding Training Data Reconstruction in DP-SGD Jamie Hayes Saeed Mahloujifar Borja Balle AAML FedML 81 43 0 14 Feb 2023
Private GANs, Revisited Alex Bie Gautam Kamath Guojun Zhang 104 16 0 06 Feb 2023
On the Efficacy of Differentially Private Few-shot Image Classification Marlon Tobaben Aliaksandra Shysheya J. Bronskill Andrew Paverd Shruti Tople Santiago Zanella Béguelin Richard Turner Antti Honkela 96 12 0 02 Feb 2023
Near Optimal Private and Robust Linear Regression Xiyang Liu Prateek Jain Weihao Kong Sewoong Oh A. Suggala 73 9 0 30 Jan 2023
Practical Differentially Private Hyperparameter Tuning with Subsampling A. Koskela Tejas D. Kulkarni 110 17 0 27 Jan 2023
Differentially Private Natural Language Models: Recent Advances and Future Directions Lijie Hu Ivan Habernal Lei Shen Di Wang AAML 92 19 0 22 Jan 2023
A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization Ashwinee Panda Xinyu Tang Saeed Mahloujifar Vikash Sehwag Prateek Mittal 126 12 0 08 Dec 2022
Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping Jiyan He Xuechen Li Da Yu Huishuai Zhang Janardhan Kulkarni Y. Lee A. Backurs Nenghai Yu Jiang Bian 118 49 0 03 Dec 2022
Private Ad Modeling with DP-SGD Carson E. Denison Badih Ghazi Pritish Kamath Ravi Kumar Pasin Manurangsi Krishnagiri Narra Amer Sinha A. Varadarajan Chiyuan Zhang 84 14 0 21 Nov 2022
Privately Fine-Tuning Large Language Models with Differential Privacy R. Behnia Mohammadreza Ebrahimi Jason L. Pacheco B. Padmanabhan 125 50 0 26 Oct 2022
Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe Xiang Yue Huseyin A. Inan Xuechen Li Girish Kumar Julia McAnallen Hoda Shajari Huan Sun David Levitan Robert Sim 152 86 0 25 Oct 2022
Differentially Private Language Models for Secure Data Sharing Justus Mattern Zhijing Jin Benjamin Weggenmann Bernhard Schoelkopf Mrinmaya Sachan SyDa 104 52 0 25 Oct 2022
Generalised Likelihood Ratio Testing Adversaries through the Differential Privacy Lens Georgios Kaissis Alexander Ziller Stefan Kolek Martinez de Azagra Daniel Rueckert 51 0 0 24 Oct 2022
Differentially Private Diffusion Models Tim Dockhorn Tianshi Cao Arash Vahdat Karsten Kreis DiffM 89 100 0 18 Oct 2022
A Closer Look at the Calibration of Differentially Private Learners Hanlin Zhang Xuechen Li Prithviraj Sen Salim Roukos Tatsunori Hashimoto 73 3 0 15 Oct 2022
Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies Zhanyu Wang Guang Cheng Jordan Awan 92 9 0 12 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