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Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data

18 October 2016

Papers citing "Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data"

50 / 353 papers shown

Title
Knowledge Distillation as Semiparametric Inference Tri Dao G. Kamath Vasilis Syrgkanis Lester W. Mackey 88 32 0 20 Apr 2021
DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation Wei Ping Fan Wu Yunhui Long Luka Rimanic Ce Zhang Yue Liu FedML 140 66 0 20 Mar 2021
Membership Inference Attacks on Machine Learning: A Survey Hongsheng Hu Z. Salcic Lichao Sun Gillian Dobbie Philip S. Yu Xuyun Zhang MIACV 125 449 0 14 Mar 2021
Learning by Teaching, with Application to Neural Architecture Search Parth Sheth Yueyu Jiang P. Xie 62 4 0 11 Mar 2021
Quantum machine learning with differential privacy William Watkins Samuel Yen-Chi Chen Shinjae Yoo 95 49 0 10 Mar 2021
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning Shaoxiong Ji Yue Tan Teemu Saravirta Zhiqin Yang Yixin Liu Lauri Vasankari Shirui Pan Guodong Long A. Walid FedML 159 79 0 25 Feb 2021
Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning Da Yu Huishuai Zhang Wei Chen Tie-Yan Liu FedML SILM 169 116 0 25 Feb 2021
PRICURE: Privacy-Preserving Collaborative Inference in a Multi-Party Setting Ismat Jarin Birhanu Eshete 93 20 0 19 Feb 2021
Differential Privacy and Byzantine Resilience in SGD: Do They Add Up? R. Guerraoui Nirupam Gupta Rafael Pinot Sébastien Rouault John Stephan 76 30 0 16 Feb 2021
Machine Learning Based Cyber Attacks Targeting on Controlled Information: A Survey Yuantian Miao Chao Chen Lei Pan Qing-Long Han Jun Zhang Yang Xiang AAML 111 68 0 16 Feb 2021
Deep Learning with Label Differential Privacy Badih Ghazi Noah Golowich Ravi Kumar Pasin Manurangsi Chiyuan Zhang 120 153 0 11 Feb 2021
CaPC Learning: Confidential and Private Collaborative Learning Christopher A. Choquette-Choo Natalie Dullerud Adam Dziedzic Yunxiang Zhang S. Jha Nicolas Papernot Xiao Wang FedML 130 58 0 09 Feb 2021
ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models Yugeng Liu Rui Wen Xinlei He A. Salem Zhikun Zhang Michael Backes Emiliano De Cristofaro Mario Fritz Yang Zhang AAML 109 134 0 04 Feb 2021
Decentralized Federated Learning Preserves Model and Data Privacy Thorsten Wittkopp Alexander Acker 75 20 0 01 Feb 2021
Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversary Jean-Francois Rajotte Soumendu Sundar Mukherjee Caleb Robinson Anthony Ortiz Christopher West J. L. Ferres R. Ng FedML MedIm 184 41 0 18 Jan 2021
Differentially Private Synthetic Medical Data Generation using Convolutional GANs A. Torfi Edward A. Fox Chandan K. Reddy SyDa MedIm 88 127 0 22 Dec 2020
Synthetic Data: Opening the data floodgates to enable faster, more directed development of machine learning methods James Jordon A. Wilson M. Schaar AI4CE 141 18 0 08 Dec 2020
Privacy and Robustness in Federated Learning: Attacks and Defenses Lingjuan Lyu Han Yu Xingjun Ma Chen Chen Lichao Sun Jun Zhao Qiang Yang Philip S. Yu FedML 331 380 0 07 Dec 2020
TransMIA: Membership Inference Attacks Using Transfer Shadow Training Seira Hidano Takao Murakami Yusuke Kawamoto MIACV 88 14 0 30 Nov 2020
When Machine Learning Meets Privacy: A Survey and Outlook B. Liu Ming Ding Sina shaham W. Rahayu F. Farokhi Zihuai Lin 97 293 0 24 Nov 2020
Synthetic Data -- Anonymisation Groundhog Day Theresa Stadler Bristena Oprisanu Carmela Troncoso 93 161 0 13 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 74 67 0 11 Nov 2020
Private Outsourced Bayesian Optimization D. Kharkovskii Zhongxiang Dai K. H. Low 78 21 0 24 Oct 2020
Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings Vinith Suriyakumar Nicolas Papernot Anna Goldenberg Marzyeh Ghassemi OOD 76 67 0 13 Oct 2020
Fairness-aware Agnostic Federated Learning Wei Du Depeng Xu Xintao Wu Hanghang Tong FedML 90 131 0 10 Oct 2020
Voting-based Approaches For Differentially Private Federated Learning Yuqing Zhu Xiang Yu Yi-Hsuan Tsai Francesco Pittaluga M. Faraki Manmohan Chandraker Yu Wang FedML 74 22 0 09 Oct 2020
Practical One-Shot Federated Learning for Cross-Silo Setting Qinbin Li Bingsheng He Basel Alomair FedML 86 121 0 02 Oct 2020
Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach Cuong Tran Ferdinando Fioretto Pascal Van Hentenryck FedML 85 80 0 26 Sep 2020
Training Production Language Models without Memorizing User Data Swaroop Indra Ramaswamy Om Thakkar Rajiv Mathews Galen Andrew H. B. McMahan Franccoise Beaufays FedML 84 92 0 21 Sep 2020
Learning Realistic Patterns from Unrealistic Stimuli: Generalization and Data Anonymization K. Nikolaidis Stein Kristiansen T. Plagemann V. Goebel Knut Liestøl ... G. Traaen Britt Overland Harriet Akre L. Aakerøy S. Steinshamn 25 4 0 21 Sep 2020
Private data sharing between decentralized users through the privGAN architecture Jean-Francois Rajotte R. Ng FedML 69 3 0 14 Sep 2020
Federated Model Distillation with Noise-Free Differential Privacy Lichao Sun Lingjuan Lyu FedML 107 109 0 11 Sep 2020
Local and Central Differential Privacy for Robustness and Privacy in Federated Learning Mohammad Naseri Jamie Hayes Emiliano De Cristofaro FedML 124 153 0 08 Sep 2020
Scaling up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping Jaewoo Lee Daniel Kifer 88 57 0 07 Sep 2020
FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning Hong-You Chen Wei-Lun Chao FedML 101 262 0 04 Sep 2020
Model extraction from counterfactual explanations Ulrich Aïvodji Alexandre Bolot Sébastien Gambs MIACV MLAU 87 52 0 03 Sep 2020
NoPeek: Information leakage reduction to share activations in distributed deep learning Praneeth Vepakomma Abhishek Singh O. Gupta Ramesh Raskar MIACV FedML 109 86 0 20 Aug 2020
Efficient Private Machine Learning by Differentiable Random Transformations F. Zheng 26 0 0 18 Aug 2020
Three Variants of Differential Privacy: Lossless Conversion and Applications S. Asoodeh Jiachun Liao Flavio du Pin Calmon O. Kosut Lalitha Sankar 69 39 0 14 Aug 2020
Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Data Sohei Itahara Takayuki Nishio Yusuke Koda M. Morikura Koji Yamamoto FedML 77 263 0 14 Aug 2020
Data Minimization for GDPR Compliance in Machine Learning Models Abigail Goldsteen Gilad Ezov Ron Shmelkin Micha Moffie Ariel Farkash 53 65 0 06 Aug 2020
More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence Tianqing Zhu Dayong Ye Wei Wang Wanlei Zhou Philip S. Yu SyDa 83 130 0 05 Aug 2020
Learning from Mixtures of Private and Public Populations Raef Bassily Shay Moran Anupama Nandi FedML 56 25 0 01 Aug 2020
LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy Lichao Sun Jianwei Qian Xun Chen FedML 84 214 0 31 Jul 2020
Anonymizing Machine Learning Models Abigail Goldsteen Gilad Ezov Ron Shmelkin Micha Moffie Ariel Farkash MIACV 40 5 0 26 Jul 2020
Quality Inference in Federated Learning with Secure Aggregation Balázs Pejó G. Biczók FedML 75 22 0 13 Jul 2020
The Trade-Offs of Private Prediction Laurens van der Maaten Awni Y. Hannun 103 24 0 09 Jul 2020
Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification Yingxue Zhou Zhiwei Steven Wu A. Banerjee 76 110 0 07 Jul 2020
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning Seth Neel Aaron Roth Saeed Sharifi-Malvajerdi MU 111 276 0 06 Jul 2020
P3GM: Private High-Dimensional Data Release via Privacy Preserving Phased Generative Model Shun Takagi Tsubasa Takahashi Yang Cao Masatoshi Yoshikawa SyDa 90 39 0 22 Jun 2020