Deep Learning with Differential Privacy

1 July 2016

Papers citing "Deep Learning with Differential Privacy"

50 / 1,131 papers shown

Title
Score Attack: A Lower Bound Technique for Optimal Differentially Private Learning T. Tony Cai Yichen Wang Linjun Zhang 46 16 0 13 Mar 2023
FedREP: A Byzantine-Robust, Communication-Efficient and Privacy-Preserving Framework for Federated Learning Yi-Rui Yang Kun Wang Wulu Li FedML 52 3 0 09 Mar 2023
Generative Model-Based Attack on Learnable Image Encryption for Privacy-Preserving Deep Learning AprilPyone Maungmaung Hitoshi Kiya FedML DiffM 31 3 0 09 Mar 2023
Considerations on the Theory of Training Models with Differential Privacy Marten van Dijk Phuong Ha Nguyen FedML 36 2 0 08 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
GlucoSynth: Generating Differentially-Private Synthetic Glucose Traces Josephine Lamp M. Derdzinski Christopher Hannemann Joost van der Linden Lu Feng Tianhao Wang David Evans AI4TS 29 3 0 02 Mar 2023
Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance Xin Gu Gautam Kamath Zhiwei Steven Wu 33 12 0 02 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 108 167 0 01 Mar 2023
Arbitrary Decisions are a Hidden Cost of Differentially Private Training B. Kulynych Hsiang Hsu Carmela Troncoso Flavio du Pin Calmon 30 18 0 28 Feb 2023
Differentially Private Distributed Convex Optimization Minseok Ryu Kibaek Kim FedML 33 1 0 28 Feb 2023
Differentially Private Diffusion Models Generate Useful Synthetic Images Sahra Ghalebikesabi Leonard Berrada Sven Gowal Ira Ktena Robert Stanforth Jamie Hayes Soham De Samuel L. Smith Olivia Wiles Borja Balle DiffM 40 69 0 27 Feb 2023
Active Membership Inference Attack under Local Differential Privacy in Federated Learning Truc D. T. Nguyen Phung Lai K. Tran Nhathai Phan My T. Thai FedML 32 18 0 24 Feb 2023
From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning Edwige Cyffers A. Bellet D. Basu FedML 34 5 0 24 Feb 2023
Privacy Against Hypothesis-Testing Adversaries for Quantum Computing F. Farokhi 20 2 0 24 Feb 2023
Catch Me If You Can: Semi-supervised Graph Learning for Spotting Money Laundering Md. Rezaul Karim Felix Hermsen S. Chala Paola de Perthuis Avikarsha Mandal 29 4 0 23 Feb 2023
Personalized Privacy-Preserving Framework for Cross-Silo Federated Learning Van Tuan Tran Huy Hieu Pham Kok-Seng Wong FedML 44 7 0 22 Feb 2023
A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy Yifei Zhang Dun Zeng Jinglong Luo Zenglin Xu Irwin King FedML 84 48 0 21 Feb 2023
Towards Unbounded Machine Unlearning M. Kurmanji Peter Triantafillou Jamie Hayes Eleni Triantafillou MU 28 124 0 20 Feb 2023
Certified private data release for sparse Lipschitz functions Konstantin Donhauser J. Lokna Amartya Sanyal M. Boedihardjo R. Honig Fanny Yang 46 3 0 19 Feb 2023
Learning with Impartiality to Walk on the Pareto Frontier of Fairness, Privacy, and Utility Mohammad Yaghini Patty Liu Franziska Boenisch Nicolas Papernot FedML FaML 46 8 0 17 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 37 15 0 17 Feb 2023
Multi-Task Differential Privacy Under Distribution Skew Walid Krichene Prateek Jain Shuang Song Mukund Sundararajan Abhradeep Thakurta Li Zhang FedML 43 3 0 15 Feb 2023
Tight Auditing of Differentially Private Machine Learning Milad Nasr Jamie Hayes Thomas Steinke Borja Balle Florian Tramèr Matthew Jagielski Nicholas Carlini Andreas Terzis FedML 40 52 0 15 Feb 2023
DP-BART for Privatized Text Rewriting under Local Differential Privacy Timour Igamberdiev Ivan Habernal 23 17 0 15 Feb 2023
Bounding Training Data Reconstruction in DP-SGD Jamie Hayes Saeed Mahloujifar Borja Balle AAML FedML 40 39 0 14 Feb 2023
Dataset Distillation with Convexified Implicit Gradients Noel Loo Ramin Hasani Mathias Lechner Daniela Rus DD 36 42 0 13 Feb 2023
PerAda: Parameter-Efficient Federated Learning Personalization with Generalization Guarantees Chulin Xie De-An Huang Wen-Hsuan Chu Daguang Xu Chaowei Xiao Bo Li Anima Anandkumar FedML 31 10 0 13 Feb 2023
U-Clip: On-Average Unbiased Stochastic Gradient Clipping Bryn Elesedy Marcus Hutter 21 1 0 06 Feb 2023
Private GANs, Revisited Alex Bie Gautam Kamath Guojun Zhang 40 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 23 7 0 06 Feb 2023
GAN-based Vertical Federated Learning for Label Protection in Binary Classification Yujin Han Leying Guan FedML 40 0 0 04 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 39 18 0 03 Feb 2023
Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation Noel Loo Ramin Hasani Mathias Lechner Alexander Amini Daniela Rus DD 52 5 0 02 Feb 2023
Are Diffusion Models Vulnerable to Membership Inference Attacks? Jinhao Duan Fei Kong Shiqi Wang Xiaoshuang Shi Kaidi Xu 35 109 0 02 Feb 2023
Multi-scale Feature Alignment for Continual Learning of Unlabeled Domains Kevin Thandiackal Luigi Piccinelli Pushpak Pati O. Goksel CLL OOD MedIm 34 7 0 02 Feb 2023
Privacy Risk for anisotropic Langevin dynamics using relative entropy bounds Anastasia Borovykh N. Kantas P. Parpas G. Pavliotis 19 1 0 01 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 29 214 0 01 Feb 2023
Personalized Privacy Auditing and Optimization at Test Time Cuong Tran Ferdinando Fioretto 11 2 0 31 Jan 2023
Differentially Private Distributed Bayesian Linear Regression with MCMC Barics Alparslan S. Yıldırım cS. .Ilker Birbil FedML 25 1 0 31 Jan 2023
Near Optimal Private and Robust Linear Regression Xiyang Liu Prateek Jain Weihao Kong Sewoong Oh A. Suggala 41 9 0 30 Jan 2023
Extracting Training Data from Diffusion Models Nicholas Carlini Jamie Hayes Milad Nasr Matthew Jagielski Vikash Sehwag Florian Tramèr Borja Balle Daphne Ippolito Eric Wallace DiffM 73 572 0 30 Jan 2023
FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation Hanlin Gu Jiahuan Luo Yan Kang Lixin Fan Qiang Yang FedML 36 13 0 30 Jan 2023
Context-Aware Differential Privacy for Language Modeling M. H. Dinh Ferdinando Fioretto 33 2 0 28 Jan 2023
Does Federated Learning Really Need Backpropagation? H. Feng Tianyu Pang Chao Du Wei Chen Shuicheng Yan Min Lin FedML 36 10 0 28 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
Synthcity: facilitating innovative use cases of synthetic data in different data modalities Zhaozhi Qian B. Cebere M. Schaar SyDa 43 57 0 18 Jan 2023
Threats, Vulnerabilities, and Controls of Machine Learning Based Systems: A Survey and Taxonomy Yusuke Kawamoto Kazumasa Miyake K. Konishi Y. Oiwa 29 4 0 18 Jan 2023
Label Inference Attack against Split Learning under Regression Setting Shangyu Xie Xin Yang Yuanshun Yao Tianyi Liu Taiqing Wang Jiankai Sun FedML 31 9 0 18 Jan 2023
A Fast Algorithm for Adaptive Private Mean Estimation John C. Duchi Saminul Haque Rohith Kuditipudi FedML 27 15 0 17 Jan 2023
ODIM: Outlier Detection via Likelihood of Under-Fitted Generative Models Dongha Kim Jaesung Hwang Jongjin Lee Kunwoong Kim Yongdai Kim OODD 33 1 0 11 Jan 2023