Deep Learning with Differential Privacy

1 July 2016

Papers citing "Deep Learning with Differential Privacy"

50 / 1,162 papers shown

Title
Free Gap Estimates from the Exponential Mechanism, Sparse Vector, Noisy Max and Related Algorithms Zeyu Ding Yuxin Wang Yingtai Xiao Guanhong Wang Danfeng Zhang Daniel Kifer 36 6 0 02 Dec 2020
TransMIA: Membership Inference Attacks Using Transfer Shadow Training Seira Hidano Takao Murakami Yusuke Kawamoto MIACV 33 13 0 30 Nov 2020
Distributed Additive Encryption and Quantization for Privacy Preserving Federated Deep Learning Hangyu Zhu Rui Wang Yaochu Jin K. Liang Jianting Ning FedML 40 46 0 25 Nov 2020
Advancements of federated learning towards privacy preservation: from federated learning to split learning Chandra Thapa Pathum Chamikara Mahawaga Arachchige S. Çamtepe FedML 34 84 0 25 Nov 2020
Strong Data Augmentation Sanitizes Poisoning and Backdoor Attacks Without an Accuracy Tradeoff Eitan Borgnia Valeriia Cherepanova Liam H. Fowl Amin Ghiasi Jonas Geiping Micah Goldblum Tom Goldstein Arjun Gupta AAML 24 127 0 18 Nov 2020
A Distributed Differentially Private Algorithm for Resource Allocation in Unboundedly Large Settings Panayiotis Danassis Aleksei Triastcyn Boi Faltings 11 4 0 16 Nov 2020
A Distributed Privacy-Preserving Learning Dynamics in General Social Networks Youming Tao Shuzhen Chen Feng Li Dongxiao Yu Jiguo Yu Hao Sheng FedML 19 3 0 15 Nov 2020
CatFedAvg: Optimising Communication-efficiency and Classification Accuracy in Federated Learning D. Sarkar Sumit Rai Ankur Narang FedML 26 2 0 14 Nov 2020
Differentially Private Synthetic Data: Applied Evaluations and Enhancements Lucas Rosenblatt Xiao-Yang Liu Samira Pouyanfar Eduardo de Leon Anuj M. Desai Joshua Allen SyDa 29 64 0 11 Nov 2020
Is Private Learning Possible with Instance Encoding? Nicholas Carlini Samuel Deng Sanjam Garg S. Jha Saeed Mahloujifar Mohammad Mahmoody Shuang Song Abhradeep Thakurta Florian Tramèr MIACV 32 38 0 10 Nov 2020
Compression Boosts Differentially Private Federated Learning Raouf Kerkouche G. Ács C. Castelluccia P. Genevès FedML 30 29 0 10 Nov 2020
Interpretable collaborative data analysis on distributed data A. Imakura Hiroaki Inaba Yukihiko Okada Tetsuya Sakurai FedML 19 26 0 09 Nov 2020
The Cost of Privacy in Generalized Linear Models: Algorithms and Minimax Lower Bounds T. Tony Cai Yichen Wang Linjun Zhang FedML 43 20 0 08 Nov 2020
On the Privacy Risks of Algorithmic Fairness Hong Chang Reza Shokri FaML 38 110 0 07 Nov 2020
FederBoost: Private Federated Learning for GBDT Zhihua Tian Rui Zhang Xiaoyang Hou Jian-wei Liu K. Ren Jian Liu Kui Ren FedML AI4CE 47 66 0 05 Nov 2020
The Limits of Differential Privacy (and its Misuse in Data Release and Machine Learning) J. Domingo-Ferrer David Sánchez Alberto Blanco-Justicia 29 106 0 04 Nov 2020
A Scalable Approach for Privacy-Preserving Collaborative Machine Learning Jinhyun So Başak Güler A. Avestimehr FedML 33 50 0 03 Nov 2020
One-Shot Federated Learning with Neuromorphic Processors Kenneth Stewart Yanqi Gu FedML 16 2 0 01 Nov 2020
Differentially Private Bayesian Inference for Generalized Linear Models Tejas D. Kulkarni Joonas Jälkö A. Koskela Samuel Kaski Antti Honkela 40 31 0 01 Nov 2020
Differentially Private ADMM Algorithms for Machine Learning Tao Xu Fanhua Shang Yuanyuan Liu Hongying Liu Longjie Shen Maoguo Gong 38 17 0 31 Oct 2020
Dataset Meta-Learning from Kernel Ridge-Regression Timothy Nguyen Zhourung Chen Jaehoon Lee DD 36 241 0 30 Oct 2020
Throughput-Optimal Topology Design for Cross-Silo Federated Learning Othmane Marfoq Chuan Xu Giovanni Neglia Richard Vidal FedML 73 85 0 23 Oct 2020
On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data Di Wang Hanshen Xiao S. Devadas Jinhui Xu 35 56 0 21 Oct 2020
Mitigating Sybil Attacks on Differential Privacy based Federated Learning Yupeng Jiang Yong Li Yipeng Zhou Xi Zheng FedML AAML 29 15 0 20 Oct 2020
Non-Stochastic Private Function Evaluation F. Farokhi G. Nair 29 3 0 20 Oct 2020
Privacy-preserving Data Sharing on Vertically Partitioned Data Razane Tajeddine Joonas Jälkö Samuel Kaski Antti Honkela FedML 38 8 0 19 Oct 2020
From Distributed Machine Learning To Federated Learning: In The View Of Data Privacy And Security Sheng Shen Tianqing Zhu Di Wu Wei Wang Wanlei Zhou FedML OOD 25 77 0 19 Oct 2020
Latent Dirichlet Allocation Model Training with Differential Privacy Fangyuan Zhao Xuebin Ren Shusen Yang Qing Han Peng Zhao Xinyu Yang 40 28 0 09 Oct 2020
Correlated Differential Privacy: Feature Selection in Machine Learning Tao Zhang Tianqing Zhu Ping Xiong Huan Huo Z. Tari Wanlei Zhou OOD 15 65 0 07 Oct 2020
InstaHide: Instance-hiding Schemes for Private Distributed Learning Yangsibo Huang Zhao Song Keqin Li Sanjeev Arora FedML PICV 25 150 0 06 Oct 2020
PCAL: A Privacy-preserving Intelligent Credit Risk Modeling Framework Based on Adversarial Learning Yu Zheng Zhenyu Wu Ye Yuan Tianlong Chen Zhangyang Wang 29 12 0 06 Oct 2020
Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness Lingjuan Lyu Xuanli He Yitong Li 40 89 0 03 Oct 2020
Practical One-Shot Federated Learning for Cross-Silo Setting Qinbin Li Bingsheng He D. Song FedML 24 114 0 02 Oct 2020
GECKO: Reconciling Privacy, Accuracy and Efficiency in Embedded Deep Learning Vasisht Duddu A. Boutet Virat Shejwalkar GNN 24 4 0 02 Oct 2020
Quantifying Privacy Leakage in Graph Embedding Vasisht Duddu A. Boutet Virat Shejwalkar MIACV 17 120 0 02 Oct 2020
Machine Learning in Event-Triggered Control: Recent Advances and Open Issues Leila Sedghi Zohaib Ijaz Md. Noor-A.-Rahim K. Witheephanich Dirk Pesch AI4CE 36 15 0 27 Sep 2020
Federated Learning for Computational Pathology on Gigapixel Whole Slide Images Ming Y. Lu Dehan Kong Jana Lipkova Richard J. Chen Rajendra Singh Drew F. K. Williamson Tiffany Y. Chen Faisal Mahmood FedML MedIm 33 169 0 21 Sep 2020
Privacy-Preserving Machine Learning Training in Aggregation Scenarios Liehuang Zhu Xiangyun Tang Meng Shen Jie Zhang Xiaojiang Du 32 4 0 21 Sep 2020
Federated Dynamic GNN with Secure Aggregation Meng Jiang Taeho Jung Ryan Karl Tong Zhao FedML 16 31 0 15 Sep 2020
A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning Processes Yuxin Ma Arlen Fan Jingrui He A. R. Nelakurthi Ross Maciejewski 22 25 0 15 Sep 2020
Private data sharing between decentralized users through the privGAN architecture Jean-Francois Rajotte R. Ng FedML 34 3 0 14 Sep 2020
Differentially Private Language Models Benefit from Public Pre-training Gavin Kerrigan Dylan Slack Jens Tuyls 24 56 0 13 Sep 2020
Trading Data For Learning: Incentive Mechanism For On-Device Federated Learning Rui Hu Yanmin Gong FedML 28 63 0 11 Sep 2020
Machine Unlearning for Random Forests Jonathan Brophy Daniel Lowd MU 24 159 0 11 Sep 2020
Federated Model Distillation with Noise-Free Differential Privacy Lichao Sun Lingjuan Lyu FedML 34 106 0 11 Sep 2020
Improving Robustness to Model Inversion Attacks via Mutual Information Regularization Tianhao Wang Yuheng Zhang R. Jia 30 74 0 11 Sep 2020
Neither Private Nor Fair: Impact of Data Imbalance on Utility and Fairness in Differential Privacy Tom Farrand Fatemehsadat Mireshghallah Sahib Singh Andrew Trask FedML 11 88 0 10 Sep 2020
Hybrid Differentially Private Federated Learning on Vertically Partitioned Data Chang Wang Jian Liang Mingkai Huang Bing Bai Kun Bai Hao Li FedML 23 39 0 06 Sep 2020
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching Jonas Geiping Liam H. Fowl Wenjie Huang W. Czaja Gavin Taylor Michael Moeller Tom Goldstein AAML 21 215 0 04 Sep 2020
Sampling Attacks: Amplification of Membership Inference Attacks by Repeated Queries Shadi Rahimian Tribhuvanesh Orekondy Mario Fritz MIACV 19 25 0 01 Sep 2020