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 / 215 papers shown

Title
A Review of Privacy-preserving Federated Learning for the Internet-of-Things Christopher Briggs Zhong Fan Péter András 28 15 0 24 Apr 2020
Private Query Release Assisted by Public Data Raef Bassily Albert Cheu Shay Moran Aleksandar Nikolov Jonathan R. Ullman Zhiwei Steven Wu 76 47 0 23 Apr 2020
Systematic Evaluation of Privacy Risks of Machine Learning Models Liwei Song Prateek Mittal MIACV 196 359 0 24 Mar 2020
Differentially Private Deep Learning with Smooth Sensitivity Lichao Sun Yingbo Zhou Philip S. Yu Caiming Xiong FedML 21 9 0 01 Mar 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
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 22 38 0 16 Jan 2020
Artificial Intelligence for Social Good: A Survey Zheyuan Ryan Shi Claire Wang Fei Fang AI4TS 32 81 0 07 Jan 2020
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
An Adaptive and Fast Convergent Approach to Differentially Private Deep Learning Zhiying Xu Shuyu Shi A. Liu Jun Zhao Lin Chen FedML 29 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
Federated Learning with Bayesian Differential Privacy Aleksei Triastcyn Boi Faltings FedML 19 174 0 22 Nov 2019
Privacy Leakage Avoidance with Switching Ensembles R. Izmailov Peter Lin Chris Mesterharm S. Basu 25 2 0 18 Nov 2019
Distribution Density, Tails, and Outliers in Machine Learning: Metrics and Applications Nicholas Carlini Ulfar Erlingsson Nicolas Papernot OOD OODD 26 62 0 29 Oct 2019
Secure Evaluation of Quantized Neural Networks Anders Dalskov Daniel E. Escudero Marcel Keller 17 137 0 28 Oct 2019
Model Compression with Two-stage Multi-teacher Knowledge Distillation for Web Question Answering System Ze Yang Linjun Shou Ming Gong Wutao Lin Daxin Jiang 28 92 0 18 Oct 2019
A blockchain-orchestrated Federated Learning architecture for healthcare consortia Jonathan Passerat-Palmbach Tyler Farnan Robert C Miller M. Gross H. Flannery Bill Gleim FedML 14 54 0 12 Oct 2019
PPGAN: Privacy-preserving Generative Adversarial Network Yi Liu Jialiang Peng James J. Q. Yu Yi Wu 32 70 0 04 Oct 2019
Alleviating Privacy Attacks via Causal Learning Shruti Tople Amit Sharma A. Nori MIACV OOD 33 32 0 27 Sep 2019
Synthetic Data for Deep Learning Sergey I. Nikolenko 46 348 0 25 Sep 2019
Local Differential Privacy for Deep Learning Pathum Chamikara Mahawaga Arachchige P. Bertók I. Khalil Dongxi Liu S. Çamtepe Mohammed Atiquzzaman 41 220 0 08 Aug 2019
A Federated Learning Approach for Mobile Packet Classification Evita Bakopoulou Bálint Tillman A. Markopoulou 21 30 0 30 Jul 2019
DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM Bao Wang Quanquan Gu M. Boedihardjo Farzin Barekat Stanley J. Osher 16 25 0 28 Jun 2019
Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks Tribhuvanesh Orekondy Bernt Schiele Mario Fritz AAML 13 164 0 26 Jun 2019
G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators Yunhui Long Wei Ping Zhuolin Yang B. Kailkhura Aston Zhang C.A. Gunter Bo-wen Li 22 72 0 21 Jun 2019
Does Learning Require Memorization? A Short Tale about a Long Tail Vitaly Feldman TDI 58 482 0 12 Jun 2019
SoK: Differential Privacies Damien Desfontaines Balázs Pejó 33 122 0 04 Jun 2019
Differentially Private Learning with Adaptive Clipping Galen Andrew Om Thakkar H. B. McMahan Swaroop Ramaswamy FedML 30 331 0 09 May 2019
Mimic Learning to Generate a Shareable Network Intrusion Detection Model Ahmed A. Shafee Mohamed Baza Douglas A. Talbert M. Fouda Mahmoud Nabil Mohamed Mahmoud 36 29 0 02 May 2019
Privacy-preserving Active Learning on Sensitive Data for User Intent Classification Oluwaseyi Feyisetan Thomas Drake Borja Balle Tom Diethe 14 10 0 26 Mar 2019
One-Shot Federated Learning Neel Guha Ameet Talwalkar Virginia Smith FedML 30 212 0 28 Feb 2019
Evaluating Differentially Private Machine Learning in Practice Bargav Jayaraman David Evans 15 7 0 24 Feb 2019
Differentially Private Generative Adversarial Networks for Time Series, Continuous, and Discrete Open Data Lorenzo Frigerio Anderson Santana de Oliveira L. Gomez Patrick Duverger SyDa AI4TS 28 110 0 08 Jan 2019
A General Approach to Adding Differential Privacy to Iterative Training Procedures H. B. McMahan Galen Andrew Ulfar Erlingsson Steve Chien Ilya Mironov Nicolas Papernot Peter Kairouz 19 192 0 15 Dec 2018
No Peek: A Survey of private distributed deep learning Praneeth Vepakomma Tristan Swedish Ramesh Raskar O. Gupta Abhimanyu Dubey SyDa FedML 30 100 0 08 Dec 2018
Differentially Private Data Generative Models Qingrong Chen Chong Xiang Minhui Xue Bo-wen Li Nikita Borisov Dali Kaafar Haojin Zhu SyDa AAML 15 79 0 06 Dec 2018
Split learning for health: Distributed deep learning without sharing raw patient data Praneeth Vepakomma O. Gupta Tristan Swedish Ramesh Raskar FedML 63 692 0 03 Dec 2018
An overview of deep learning in medical imaging focusing on MRI A. Lundervold A. Lundervold OOD 22 1,608 0 25 Nov 2018
Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference Edward Chou Josh Beal Daniel Levy Serena Yeung Albert Haque Li Fei-Fei 32 198 0 25 Nov 2018
Private Model Compression via Knowledge Distillation Ji Wang Weidong Bao Lichao Sun Xiaomin Zhu Bokai Cao Philip S. Yu FedML 6 116 0 13 Nov 2018
A generic framework for privacy preserving deep learning Wenbo Guo Yunzhe Tao Morten Dahl Sui Huang Masashi Sugiyama Daniel Rueckert Lin Lin FedML 15 428 0 09 Nov 2018
Private Machine Learning in TensorFlow using Secure Computation Morten Dahl Jason V. Mancuso Yann Dupis Ben Decoste Morgan Giraud Ian Livingstone Justin Patriquin Gavin Uhma FedML 18 75 0 18 Oct 2018
Distributed learning of deep neural network over multiple agents O. Gupta Ramesh Raskar FedML OOD 17 597 0 14 Oct 2018
Deep Learning Towards Mobile Applications Ji Wang Bokai Cao Philip S. Yu Lichao Sun Weidong Bao Xiaomin Zhu HAI 32 98 0 10 Sep 2018
Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloud Ji Wang Jianguo Zhang Weidong Bao Xiaomin Zhu Bokai Cao Philip S. Yu 26 193 0 10 Sep 2018
Privacy Amplification by Iteration Vitaly Feldman Ilya Mironov Kunal Talwar Abhradeep Thakurta FedML 23 170 0 20 Aug 2018
Differentially-Private "Draw and Discard" Machine Learning Vasyl Pihur Aleksandra Korolova Frederick Liu Subhash Sankuratripati M. Yung Dachuan Huang Ruogu Zeng FedML 33 39 0 11 Jul 2018
Towards Robust Evaluations of Continual Learning Sebastian Farquhar Y. Gal CLL 28 305 0 24 May 2018
Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning Tribhuvanesh Orekondy Seong Joon Oh Yang Zhang Bernt Schiele Mario Fritz PICV FedML 359 37 0 15 May 2018
Exploiting Unintended Feature Leakage in Collaborative Learning Luca Melis Congzheng Song Emiliano De Cristofaro Vitaly Shmatikov FedML 81 1,455 0 10 May 2018
Privacy-preserving Prediction Cynthia Dwork Vitaly Feldman 25 90 0 27 Mar 2018