Deep Learning with Differential Privacy

1 July 2016

Papers citing "Deep Learning with Differential Privacy"

50 / 1,155 papers shown

Title
SplitFed: When Federated Learning Meets Split Learning Chandra Thapa Pathum Chamikara Mahawaga Arachchige S. Çamtepe Lichao Sun FedML 41 570 0 25 Apr 2020
A Review of Privacy-preserving Federated Learning for the Internet-of-Things Christopher Briggs Zhong Fan Péter András 31 15 0 24 Apr 2020
Computing Local Sensitivities of Counting Queries with Joins Yuchao Tao Xi He Ashwin Machanavajjhala Sudeepa Roy 6 37 0 09 Apr 2020
Gradient Centralization: A New Optimization Technique for Deep Neural Networks Hongwei Yong Jianqiang Huang Xiansheng Hua Lei Zhang ODL 32 184 0 03 Apr 2020
An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies David Enthoven Zaid Al-Ars FedML 60 50 0 01 Apr 2020
Information Leakage in Embedding Models Congzheng Song A. Raghunathan MIACV 27 263 0 31 Mar 2020
Differentially Private Naive Bayes Classifier using Smooth Sensitivity Farzad Zafarani Chris Clifton 6 10 0 31 Mar 2020
Sequential Transfer Machine Learning in Networks: Measuring the Impact of Data and Neural Net Similarity on Transferability Robin Hirt Akash Srivastava Carlos Berg Niklas Kühl 34 3 0 29 Mar 2020
Semi-Federated Learning Zhikun Chen Daofeng Li Mingde Zhao Sihai Zhang Jinkang Zhu FedML 16 18 0 28 Mar 2020
HERS: Homomorphically Encrypted Representation Search Joshua J. Engelsma Anil K. Jain Vishnu Boddeti 43 51 0 27 Mar 2020
Systematic Evaluation of Privacy Risks of Machine Learning Models Liwei Song Prateek Mittal MIACV 196 362 0 24 Mar 2020
Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning Shaoxiong Ji Wenqi Jiang A. Walid Xue Li FedML 33 66 0 21 Mar 2020
Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training? Sharif Abuadbba Kyuyeon Kim Minki Kim Chandra Thapa S. Çamtepe Yansong Gao Hyoungshick Kim Surya Nepal FedML 8 123 0 16 Mar 2020
Deep generative models in DataSHIELD S. Lenz Harald Binder FedML SyDa OOD MedIm 10 18 0 11 Mar 2020
Towards Probabilistic Verification of Machine Unlearning David M. Sommer Liwei Song Sameer Wagh Prateek Mittal AAML 13 71 0 09 Mar 2020
A Safety Framework for Critical Systems Utilising Deep Neural Networks Xingyu Zhao Alec Banks James Sharp Valentin Robu David Flynn Michael Fisher Xiaowei Huang AAML 55 48 0 07 Mar 2020
Forgetting Outside the Box: Scrubbing Deep Networks of Information Accessible from Input-Output Observations Aditya Golatkar Alessandro Achille Stefano Soatto MU OOD 33 189 0 05 Mar 2020
Threats to Federated Learning: A Survey Lingjuan Lyu Han Yu Qiang Yang FedML 204 436 0 04 Mar 2020
Differentially Private Deep Learning with Smooth Sensitivity Lichao Sun Yingbo Zhou Philip S. Yu Caiming Xiong FedML 29 9 0 01 Mar 2020
User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization Kang Wei Jun Li Ming Ding Chuan Ma Hang Su Bo Zhang H. Vincent Poor FedML 25 11 0 29 Feb 2020
DP-MERF: Differentially Private Mean Embeddings with Random Features for Practical Privacy-Preserving Data Generation Frederik Harder Kamil Adamczewski Mijung Park SyDa 25 101 0 26 Feb 2020
On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping Sanghyun Hong Varun Chandrasekaran Yigitcan Kaya Tudor Dumitras Nicolas Papernot AAML 28 136 0 26 Feb 2020
Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees Richeng Jin Yufan Huang Xiaofan He H. Dai Tianfu Wu FedML 27 61 0 25 Feb 2020
Coherent Gradients: An Approach to Understanding Generalization in Gradient Descent-based Optimization S. Chatterjee ODL OOD 13 51 0 25 Feb 2020
Anonymizing Data for Privacy-Preserving Federated Learning Olivia Choudhury A. Gkoulalas-Divanis Theodoros Salonidis I. Sylla Yoonyoung Park Grace Hsu Amar K. Das FedML 30 42 0 21 Feb 2020
Data Heterogeneity Differential Privacy: From Theory to Algorithm Yilin Kang Jian Li Yong Liu Weiping Wang 36 1 0 20 Feb 2020
Input Perturbation: A New Paradigm between Central and Local Differential Privacy Yilin Kang Yong Liu Ben Niu Xin-Yi Tong Likun Zhang Weiping Wang 29 11 0 20 Feb 2020
Federated Extra-Trees with Privacy Preserving Yang Liu Mingxi Chen Wenxi Zhang Junbo Zhang Yu Zheng FedML 28 3 0 18 Feb 2020
LinkedIn's Audience Engagements API: A Privacy Preserving Data Analytics System at Scale Ryan M. Rogers S. Subramaniam Sean Peng D. Durfee Seunghyun Lee Santosh Kumar Kancha Shraddha Sahay P. Ahammad 30 77 0 14 Feb 2020
Salvaging Federated Learning by Local Adaptation Tao Yu Eugene Bagdasaryan Vitaly Shmatikov FedML 25 262 0 12 Feb 2020
Guidelines for Implementing and Auditing Differentially Private Systems Daniel Kifer Solomon Messing Aaron Roth Abhradeep Thakurta Danfeng Zhang 21 34 0 10 Feb 2020
Machine Unlearning: Linear Filtration for Logit-based Classifiers Thomas Baumhauer Pascal Schöttle Matthias Zeppelzauer MU 114 130 0 07 Feb 2020
Pure Differentially Private Summation from Anonymous Messages Badih Ghazi Noah Golowich Ravi Kumar Pasin Manurangsi Rasmus Pagh A. Velingker FedML 41 46 0 05 Feb 2020
Radioactive data: tracing through training Alexandre Sablayrolles Matthijs Douze Cordelia Schmid Hervé Jégou 38 74 0 03 Feb 2020
On the Convergence of Artificial Intelligence and Distributed Ledger Technology: A Scoping Review and Future Research Agenda Konstantin D. Pandl Scott Thiebes Manuel Schmidt-Kraepelin Ali Sunyaev 38 69 0 29 Jan 2020
A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via $f$ -Divergences S. Asoodeh Jiachun Liao Flavio du Pin Calmon O. Kosut Lalitha Sankar FedML 30 38 0 16 Jan 2020
Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results Xiaoxiao Li Yufeng Gu Nicha Dvornek Lawrence H. Staib P. Ventola James S. Duncan FedML OOD 20 353 0 16 Jan 2020
Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Shuang Song Kunal Talwar Abhradeep Thakurta 34 83 0 10 Jan 2020
Towards Deep Federated Defenses Against Malware in Cloud Ecosystems Josh Payne A. Kundu FedML 26 10 0 27 Dec 2019
Federated machine learning with Anonymous Random Hybridization (FeARH) on medical records Jianfei Cui He Zhu Hao Deng Ziwei Chen Dianbo Liu 23 33 0 25 Dec 2019
Assessing differentially private deep learning with Membership Inference Daniel Bernau Philip-William Grassal J. Robl Florian Kerschbaum MIACV FedML 26 23 0 24 Dec 2019
Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer Hong Chang Virat Shejwalkar Reza Shokri Amir Houmansadr FedML 26 167 0 24 Dec 2019
A Survey on Distributed Machine Learning Joost Verbraeken Matthijs Wolting Jonathan Katzy Jeroen Kloppenburg Tim Verbelen Jan S. Rellermeyer OOD 44 697 0 20 Dec 2019
Segmentations-Leak: Membership Inference Attacks and Defenses in Semantic Image Segmentation Yang He Shadi Rahimian Bernt Schiele Mario Fritz MIACV 21 49 0 20 Dec 2019
DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News Xinyi Li Yinchuan Li Hongyang Yang Liuqing Yang Xiao-Yang Liu AIFin 13 49 0 20 Dec 2019
An Adaptive and Fast Convergent Approach to Differentially Private Deep Learning Zhiying Xu Shuyu Shi A. Liu Jun Zhao Lin Chen FedML 47 36 0 19 Dec 2019
Asynchronous Federated Learning with Differential Privacy for Edge Intelligence Yanan Li Shusen Yang Xuebin Ren Cong Zhao FedML 19 33 0 17 Dec 2019
Private Federated Learning with Domain Adaptation Daniel W. Peterson Pallika H. Kanani Virendra J. Marathe FedML 23 81 0 13 Dec 2019
HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning Runhua Xu Nathalie Baracaldo Yi Zhou Ali Anwar Heiko Ludwig FedML 24 287 0 12 Dec 2019
Machine Unlearning Lucas Bourtoule Varun Chandrasekaran Christopher A. Choquette-Choo Hengrui Jia Adelin Travers Baiwu Zhang David Lie Nicolas Papernot MU 65 818 0 09 Dec 2019