Numerical Composition of Differential Privacy

5 June 2021

Papers citing "Numerical Composition of Differential Privacy"

50 / 51 papers shown

Title
On the Price of Differential Privacy for Spectral Clustering over Stochastic Block Models Antti Koskela Mohamed Seif Andrea J. Goldsmith 31 1 0 09 May 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
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
RAPID: Retrieval Augmented Training of Differentially Private Diffusion Models Tanqiu Jiang Changjiang Li Fenglong Ma Ting Wang 70 0 0 18 Feb 2025
Adversarial Sample-Based Approach for Tighter Privacy Auditing in Final Model-Only Scenarios Sangyeon Yoon Wonje Jeung Albert No 87 0 0 02 Dec 2024
Noise-Aware Differentially Private Variational Inference Talal Alrawajfeh Joonas Jälkö Antti Honkela 35 0 0 25 Oct 2024
The 2020 United States Decennial Census Is More Private Than You (Might) Think Buxin Su Weijie J. Su Chendi Wang 39 3 0 11 Oct 2024
Differentially Private Active Learning: Balancing Effective Data Selection and Privacy Kristian Schwethelm Johannes Kaiser Jonas Kuntzer Mehmet Yigitsoy Daniel Rueckert Georgios Kaissis 37 0 0 01 Oct 2024
Beyond the Calibration Point: Mechanism Comparison in Differential Privacy Georgios Kaissis Stefan Kolek Borja Balle Jamie Hayes Daniel Rueckert 47 4 0 13 Jun 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
Visual Privacy Auditing with Diffusion Models Kristian Schwethelm Johannes Kaiser Moritz Knolle Daniel Rueckert Daniel Rueckert Alexander Ziller DiffM AAML 35 0 0 12 Mar 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
Tight Group-Level DP Guarantees for DP-SGD with Sampling via Mixture of Gaussians Mechanisms Arun Ganesh 26 2 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 47 18 0 09 Jan 2024
Privately Aligning Language Models with Reinforcement Learning Fan Wu Huseyin A. Inan A. Backurs Varun Chandrasekaran Janardhan Kulkarni Robert Sim 29 6 0 25 Oct 2023
Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy Georgios Kaissis Jamie Hayes Alexander Ziller Daniel Rueckert AAML 43 11 0 08 Jul 2023
Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD Anvith Thudi Hengrui Jia Casey Meehan Ilia Shumailov Nicolas Papernot 33 3 0 01 Jul 2023
Differentially Private Image Classification by Learning Priors from Random Processes Xinyu Tang Ashwinee Panda Vikash Sehwag Prateek Mittal 23 20 0 08 Jun 2023
PILLAR: How to make semi-private learning more effective Francesco Pinto Yaxian Hu Fanny Yang Amartya Sanyal 52 11 0 06 Jun 2023
Jointly Reparametrized Multi-Layer Adaptation for Efficient and Private Tuning Umang Gupta Aram Galstyan Greg Ver Steeg 11 2 0 30 May 2023
Differentially Private Synthetic Data via Foundation Model APIs 1: Images Zinan Lin Sivakanth Gopi Janardhan Kulkarni Harsha Nori Sergey Yekhanin 41 37 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 38 19 0 23 May 2023
Privacy Amplification via Shuffling: Unified, Simplified, and Tightened Shaowei Wang FedML 26 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 28 18 0 28 Feb 2023
Bounding Training Data Reconstruction in DP-SGD Jamie Hayes Saeed Mahloujifar Borja Balle AAML FedML 33 39 0 14 Feb 2023
Private GANs, Revisited Alex Bie Gautam Kamath Guojun Zhang 38 14 0 06 Feb 2023
Near Optimal Private and Robust Linear Regression Xiyang Liu Prateek Jain Weihao Kong Sewoong Oh A. Suggala 41 9 0 30 Jan 2023
Differentially Private Natural Language Models: Recent Advances and Future Directions Lijie Hu Ivan Habernal Lei Shen Di Wang AAML 35 18 0 22 Jan 2023
A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization Ashwinee Panda Xinyu Tang Saeed Mahloujifar Vikash Sehwag Prateek Mittal 43 11 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 30 46 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 32 14 0 21 Nov 2022
Privately Fine-Tuning Large Language Models with Differential Privacy R. Behnia Mohammadreza Ebrahimi Jason L. Pacheco B. Padmanabhan 29 44 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 58 79 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 12 0 0 24 Oct 2022
Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies Zhanyu Wang Guang Cheng Jordan Awan 34 9 0 12 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
Federated Boosted Decision Trees with Differential Privacy Samuel Maddock Graham Cormode Tianhao Wang Carsten Maple S. Jha FedML 34 29 0 06 Oct 2022
Composition of Differential Privacy & Privacy Amplification by Subsampling Thomas Steinke 66 50 0 02 Oct 2022
Differentially Private Optimization on Large Model at Small Cost Zhiqi Bu Yu-Xiang Wang Sheng Zha George Karypis 38 52 0 30 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
Faster Privacy Accounting via Evolving Discretization Badih Ghazi Pritish Kamath Ravi Kumar Pasin Manurangsi 65 14 0 10 Jul 2022
Connect the Dots: Tighter Discrete Approximations of Privacy Loss Distributions Vadym Doroshenko Badih Ghazi Pritish Kamath Ravi Kumar Pasin Manurangsi 26 40 0 10 Jul 2022
Scaling Private Deep Learning with Low-Rank and Sparse Gradients Ryuichi Ito Seng Pei Liew Tsubasa Takahashi Yuya Sasaki Makoto Onizuka 30 1 0 06 Jul 2022
Cactus Mechanisms: Optimal Differential Privacy Mechanisms in the Large-Composition Regime Wael Alghamdi S. Asoodeh Flavio du Pin Calmon O. Kosut Lalitha Sankar Fei Wei 19 8 0 25 Jun 2022
Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger Zhiqi Bu Yu-Xiang Wang Sheng Zha George Karypis 27 69 0 14 Jun 2022
Bayesian Estimation of Differential Privacy Santiago Zanella Béguelin Lukas Wutschitz Shruti Tople A. Salem Victor Rühle Andrew J. Paverd Mohammad Naseri Boris Köpf Daniel Jones 25 36 0 10 Jun 2022
Analytical Composition of Differential Privacy via the Edgeworth Accountant Hua Wang Sheng-yang Gao Huanyu Zhang Milan Shen Weijie J. Su FedML 36 21 0 09 Jun 2022
Differentially Private Fine-tuning of Language Models Da Yu Saurabh Naik A. Backurs Sivakanth Gopi Huseyin A. Inan ... Y. Lee Andre Manoel Lukas Wutschitz Sergey Yekhanin Huishuai Zhang 134 350 0 13 Oct 2021
Large-Scale Differentially Private BERT Rohan Anil Badih Ghazi Vineet Gupta Ravi Kumar Pasin Manurangsi 36 131 0 03 Aug 2021
Optimal Accounting of Differential Privacy via Characteristic Function Yuqing Zhu Jinshuo Dong Yu-Xiang Wang 18 98 0 16 Jun 2021