Deep Learning with Differential Privacy

1 July 2016

Papers citing "Deep Learning with Differential Privacy"

50 / 1,131 papers shown

Title
Privacy and Efficiency of Communications in Federated Split Learning Zongshun Zhang Andrea Pinto Valeria Turina Flavio Esposito I. Matta FedML 40 32 0 04 Jan 2023
Differentially Private Federated Clustering over Non-IID Data Yiwei Li Shuai Wang Chong-Yung Chi Tony Q.S. Quek FedML 40 13 0 03 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
ReSQueing Parallel and Private Stochastic Convex Optimization Y. Carmon A. Jambulapati Yujia Jin Y. Lee Daogao Liu Aaron Sidford Kevin Tian FedML 41 12 0 01 Jan 2023
A Comparative Study of Image Disguising Methods for Confidential Outsourced Learning Sagar Sharma Yuechun Gu Keke Chen 39 0 0 31 Dec 2022
Deep Hierarchy Quantization Compression algorithm based on Dynamic Sampling W. Jiang Gang Liu Xiaofeng Chen Yipeng Zhou FedML 24 0 0 30 Dec 2022
Social-Aware Clustered Federated Learning with Customized Privacy Preservation Yuntao Wang Zhou Su Yanghe Pan Tom H. Luan Ruidong Li Shui Yu FedML 44 18 0 25 Dec 2022
Differentially Private Decentralized Optimization with Relay Communication Luqing Wang Luyao Guo Shaofu Yang Xinli Shi 38 0 0 21 Dec 2022
Local Differential Privacy Image Generation Using Flow-based Deep Generative Models Hisaichi Shibata S. Hanaoka Yang Cao Masatoshi Yoshikawa T. Takenaga Y. Nomura Naoto Hayashi O. Abe MedIm 30 6 0 20 Dec 2022
Learned Systems Security R. Schuster Jinyi Zhou Thorsten Eisenhofer Paul Grubbs Nicolas Papernot AAML 21 2 0 20 Dec 2022
Grafting Laplace and Gaussian distributions: A new noise mechanism for differential privacy Gokularam Muthukrishnan Sheetal Kalyani 41 12 0 19 Dec 2022
Swing Distillation: A Privacy-Preserving Knowledge Distillation Framework Junzhuo Li Xinwei Wu Weilong Dong Shuangzhi Wu Chao Bian Deyi Xiong 36 3 0 16 Dec 2022
Decentralized Nonconvex Optimization with Guaranteed Privacy and Accuracy Yongqiang Wang Tamer Basar 31 21 0 14 Dec 2022
Privacy-preserving Security Inference Towards Cloud-Edge Collaborative Using Differential Privacy Yulong Wang Xingshu Chen Qixu Wang FedML 32 3 0 13 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
Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy Ergute Bao Yizheng Zhu X. Xiao Yifan Yang Beng Chin Ooi B. Tan Khin Mi Mi Aung FedML 31 19 0 08 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
Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning Mingyuan Fan Cen Chen Chengyu Wang Ximeng Liu Wenmeng Zhou Jun Huang AAML FedML 34 0 0 05 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 41 46 0 03 Dec 2022
Membership Inference Attacks Against Semantic Segmentation Models Tomás Chobola Dmitrii Usynin Georgios Kaissis MIACV 37 6 0 02 Dec 2022
PiPar: Pipeline Parallelism for Collaborative Machine Learning Zihan Zhang Philip Rodgers Peter Kilpatrick I. Spence Blesson Varghese FedML 48 3 0 01 Dec 2022
Purifier: Defending Data Inference Attacks via Transforming Confidence Scores Ziqi Yang Li-Juan Wang D. Yang Jie Wan Ziming Zhao E. Chang Fan Zhang Kui Ren AAML 29 15 0 01 Dec 2022
Differentially Private Learning with Per-Sample Adaptive Clipping Tianyu Xia Shuheng Shen Su Yao Xinyi Fu Ke Xu Xiaolong Xu Xingbo Fu 40 16 0 01 Dec 2022
HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning Pengyu Qiu Xuhong Zhang S. Ji Chong Fu Xing Yang Ting Wang FedML AAML 35 12 0 01 Dec 2022
Differentially Private Adaptive Optimization with Delayed Preconditioners Tian Li Manzil Zaheer Ziyu Liu Sashank J. Reddi H. B. McMahan Virginia Smith 50 10 0 01 Dec 2022
Decentralized Matrix Factorization with Heterogeneous Differential Privacy Wentao Hu Hui Fang 19 0 0 01 Dec 2022
Differentially Private Enhanced Permissioned Blockchain for Private Data Sharing in Industrial IoT Muhammad Islam M. H. Rehmani Jinjun Chen 28 8 0 30 Nov 2022
The Bounded Gaussian Mechanism for Differential Privacy Bo Chen Matthew T. Hale 24 5 0 30 Nov 2022
Differentially Private ADMM-Based Distributed Discrete Optimal Transport for Resource Allocation Jason Hughes Juntao Chen OT 14 1 0 30 Nov 2022
Adap DP-FL: Differentially Private Federated Learning with Adaptive Noise Jie Fu Zhili Chen Xiao Han FedML 32 28 0 29 Nov 2022
On the Utility Recovery Incapability of Neural Net-based Differential Private Tabular Training Data Synthesizer under Privacy Deregulation Yucong Liu ChiHua Wang Guang Cheng 34 7 0 28 Nov 2022
Meta-analysis of individualized treatment rules via sign-coherency Jay Jojo Cheng J. Huling Guanhua Chen 38 0 0 28 Nov 2022
Federated Learning Attacks and Defenses: A Survey Yao Chen Yijie Gui Hong Lin Wensheng Gan Yongdong Wu FedML 49 29 0 27 Nov 2022
Differentially Private Image Classification from Features Harsh Mehta Walid Krichene Abhradeep Thakurta Alexey Kurakin Ashok Cutkosky 67 7 0 24 Nov 2022
FedCut: A Spectral Analysis Framework for Reliable Detection of Byzantine Colluders Hanlin Gu Lixin Fan Xingxing Tang Qiang Yang AAML FedML 27 1 0 24 Nov 2022
Private Multi-Winner Voting for Machine Learning Adam Dziedzic Christopher A. Choquette-Choo Natalie Dullerud Vinith Suriyakumar Ali Shahin Shamsabadi Muhammad Ahmad Kaleem S. Jha Nicolas Papernot Xiao Wang 45 1 0 23 Nov 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 37 14 0 21 Nov 2022
DPD-fVAE: Synthetic Data Generation Using Federated Variational Autoencoders With Differentially-Private Decoder Bjarne Pfitzner B. Arnrich FedML 38 19 0 21 Nov 2022
Privacy in Practice: Private COVID-19 Detection in X-Ray Images (Extended Version) Lucas Lange Maja Schneider Peter Christen Erhard Rahm 29 7 0 21 Nov 2022
Learning to Generate Image Embeddings with User-level Differential Privacy Zheng Xu Maxwell D. Collins Yuxiao Wang Liviu Panait Sewoong Oh S. Augenstein Ting Liu Florian Schroff H. B. McMahan FedML 40 29 0 20 Nov 2022
A Survey on Differential Privacy with Machine Learning and Future Outlook Samah Baraheem Z. Yao SyDa 27 1 0 19 Nov 2022
Private Federated Statistics in an Interactive Setting Audra McMillan O. Javidbakht Kunal Talwar Elliot Briggs Mike Chatzidakis ... Paul J. Pelzl Rehan Rishi Congzheng Song Shan Wang Shundong Zhou FedML 32 6 0 18 Nov 2022
SA-DPSGD: Differentially Private Stochastic Gradient Descent based on Simulated Annealing Jie Fu Zhili Chen Xinpeng Ling 37 0 0 14 Nov 2022
Provable Membership Inference Privacy Zachary Izzo Jinsung Yoon Sercan O. Arik James Zou 49 5 0 12 Nov 2022
Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning Christopher A. Choquette-Choo H. B. McMahan Keith Rush Abhradeep Thakurta 39 42 0 12 Nov 2022
Privacy-Preserving Machine Learning for Collaborative Data Sharing via Auto-encoder Latent Space Embeddings A. M. Quintero-Ossa Jesus Solano Hernán Jarcía David Zarruk Alejandro Correa-Bahnsen C. Valencia FedML 34 1 0 10 Nov 2022
Privacy Meets Explainability: A Comprehensive Impact Benchmark S. Saifullah Dominique Mercier Adriano Lucieri Andreas Dengel Sheraz Ahmed 35 14 0 08 Nov 2022
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design Chuan Guo Kamalika Chaudhuri Pierre Stock Michael G. Rabbat FedML 40 7 0 08 Nov 2022
Lessons Learned: Surveying the Practicality of Differential Privacy in the Industry Gonzalo Munilla Garrido Xiaoyuan Liu Florian Matthes D. Song 28 25 0 07 Nov 2022
Private Set Generation with Discriminative Information Dingfan Chen Raouf Kerkouche Mario Fritz DD 35 35 0 07 Nov 2022