Title
ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees Sina Sajadmanesh D. Gática-Pérez 27 15 0 18 Apr 2023
Gradient Sparsification for Efficient Wireless Federated Learning with Differential Privacy Kang Wei Jun Li Chuan Ma Ming Ding Feng Shu Haitao Zhao Wen Chen Hongbo Zhu FedML 30 4 0 09 Apr 2023
Make Landscape Flatter in Differentially Private Federated Learning Yi Shi Yingqi Liu Kang Wei Li Shen Xueqian Wang Dacheng Tao FedML 17 54 0 20 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 94 167 0 01 Mar 2023
Arbitrary Decisions are a Hidden Cost of Differentially Private Training B. Kulynych Hsiang Hsu Carmela Troncoso Flavio du Pin Calmon 22 18 0 28 Feb 2023
Privately Customizing Prefinetuning to Better Match User Data in Federated Learning Charlie Hou Hongyuan Zhan Akshat Shrivastava Sida I. Wang S. Livshits Giulia Fanti Daniel Lazar FedML 32 15 0 17 Feb 2023
Marich: A Query-efficient Distributionally Equivalent Model Extraction Attack using Public Data Pratik Karmakar D. Basu MIACV 18 6 0 16 Feb 2023
Mithridates: Auditing and Boosting Backdoor Resistance of Machine Learning Pipelines Eugene Bagdasaryan Vitaly Shmatikov AAML 24 2 0 09 Feb 2023
Pushing the Boundaries of Private, Large-Scale Query Answering Brendan Avent Aleksandra Korolova 24 0 0 09 Feb 2023
On the Privacy-Robustness-Utility Trilemma in Distributed Learning Youssef Allouah R. Guerraoui Nirupam Gupta Rafael Pinot John Stephan FedML 18 21 0 09 Feb 2023
Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness Yuancheng Xu Yanchao Sun Micah Goldblum Tom Goldstein Furong Huang AAML 28 37 0 06 Feb 2023
Private GANs, Revisited Alex Bie Gautam Kamath Guojun Zhang 27 14 0 06 Feb 2023
An Empirical Analysis of Fairness Notions under Differential Privacy Anderson Santana de Oliveira Caelin Kaplan Khawla Mallat Tanmay Chakraborty FedML 21 7 0 06 Feb 2023
Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging Soroosh Tayebi Arasteh Alexander Ziller Christiane Kuhl Marcus R. Makowski S. Nebelung R. Braren Daniel Rueckert Daniel Truhn Georgios Kaissis MedIm 37 17 0 03 Feb 2023
On the Efficacy of Differentially Private Few-shot Image Classification Marlon Tobaben Aliaksandra Shysheya J. Bronskill Andrew J. Paverd Shruti Tople Santiago Zanella Béguelin Richard Turner Antti Honkela 38 11 0 02 Feb 2023
Analyzing Leakage of Personally Identifiable Information in Language Models Nils Lukas A. Salem Robert Sim Shruti Tople Lukas Wutschitz Santiago Zanella Béguelin PILM 24 211 0 01 Feb 2023
Practical Differentially Private Hyperparameter Tuning with Subsampling A. Koskela Tejas D. Kulkarni 36 14 0 27 Jan 2023
Membership Inference of Diffusion Models Hailong Hu Jun Pang 26 37 0 24 Jan 2023
Federated Recommendation with Additive Personalization Zhiwei Li Guodong Long Tianyi Zhou FedML 36 15 0 22 Jan 2023
Cohere: Managing Differential Privacy in Large Scale Systems Nicolas Küchler Emanuel Opel Hidde Lycklama Alexander Viand Anwar Hithnawi 43 4 0 20 Jan 2023
Privacy and Efficiency of Communications in Federated Split Learning Zongshun Zhang Andrea Pinto Valeria Turina Flavio Esposito I. Matta FedML 38 32 0 04 Jan 2023
Regression with Label Differential Privacy Badih Ghazi Pritish Kamath Ravi Kumar Ethan Leeman Pasin Manurangsi A. Varadarajan Chiyuan Zhang 26 18 0 12 Dec 2022
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
Memorization of Named Entities in Fine-tuned BERT Models Andor Diera N. Lell Aygul Garifullina A. Scherp 17 0 0 07 Dec 2022
Pre-trained Encoders in Self-Supervised Learning Improve Secure and Privacy-preserving Supervised Learning Hongbin Liu Wenjie Qu Jinyuan Jia Neil Zhenqiang Gong SSL 28 6 0 06 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 27 46 0 03 Dec 2022
Differentially Private Learning with Per-Sample Adaptive Clipping Tianyu Xia Shuheng Shen Su Yao Xinyi Fu Ke Xu Xiaolong Xu Xingbo Fu 30 16 0 01 Dec 2022
SA-DPSGD: Differentially Private Stochastic Gradient Descent based on Simulated Annealing Jie Fu Zhili Chen Xinpeng Ling 25 0 0 14 Nov 2022
Directional Privacy for Deep Learning Pedro Faustini Natasha Fernandes Shakila Mahjabin Tonni Annabelle McIver Mark Dras 14 1 0 09 Nov 2022
Private Set Generation with Discriminative Information Dingfan Chen Raouf Kerkouche Mario Fritz DD 27 34 0 07 Nov 2022
Privacy-Preserving Models for Legal Natural Language Processing Ying Yin Ivan Habernal PILM AILaw 6 8 0 05 Nov 2022
Distributed DP-Helmet: Scalable Differentially Private Non-interactive Averaging of Single Layers Moritz Kirschte Sebastian Meiser Saman Ardalan Esfandiar Mohammadi FedML 34 0 0 03 Nov 2022
On the Interaction Between Differential Privacy and Gradient Compression in Deep Learning Jimmy J. Lin 11 0 0 01 Nov 2022
Local Model Reconstruction Attacks in Federated Learning and their Uses Ilias Driouich Chuan Xu Giovanni Neglia F. Giroire Eoin Thomas AAML FedML 32 2 0 28 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
Differentially Private Diffusion Models Tim Dockhorn Tianshi Cao Arash Vahdat Karsten Kreis DiffM 32 91 0 18 Oct 2022
A General Framework for Auditing Differentially Private Machine Learning Fred Lu Joseph Munoz Maya Fuchs Tyler LeBlond Elliott Zaresky-Williams Edward Raff Francis Ferraro Brian Testa FedML 22 35 0 16 Oct 2022
A Closer Look at the Calibration of Differentially Private Learners Hanlin Zhang Xuechen Li Prithviraj Sen Salim Roukos Tatsunori Hashimoto 16 3 0 15 Oct 2022
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings Jean Ogier du Terrail Samy Ayed Edwige Cyffers Felix Grimberg Chaoyang He ... Sai Praneeth Karimireddy Marco Lorenzi Giovanni Neglia Marc Tommasi M. Andreux FedML 41 142 0 10 Oct 2022
TAN Without a Burn: Scaling Laws of DP-SGD Tom Sander Pierre Stock Alexandre Sablayrolles FedML 32 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
Kernel Normalized Convolutional Networks for Privacy-Preserving Machine Learning Reza Nasirigerdeh Javad Torkzadehmahani Daniel Rueckert Georgios Kaissis 14 1 0 30 Sep 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
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 37 18 0 30 Sep 2022
M^4I: Multi-modal Models Membership Inference Pingyi Hu Zihan Wang Ruoxi Sun Hu Wang Minhui Xue 39 26 0 15 Sep 2022
Privacy-Preserving Deep Learning Model for Covid-19 Disease Detection Vijay Srinivas Tida Sonya Hsu X. Hei MedIm 42 5 0 07 Sep 2022
Data Isotopes for Data Provenance in DNNs Emily Wenger Xiuyu Li Ben Y. Zhao Vitaly Shmatikov 20 12 0 29 Aug 2022
Federated and Privacy-Preserving Learning of Accounting Data in Financial Statement Audits Marco Schreyer Timur Sattarov Damian Borth MLAU 29 15 0 26 Aug 2022
SNAP: Efficient Extraction of Private Properties with Poisoning Harsh Chaudhari John Abascal Alina Oprea Matthew Jagielski Florian Tramèr Jonathan R. Ullman MIACV 34 30 0 25 Aug 2022