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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 / 351 papers shown

Title
Decentralised Learning from Independent Multi-Domain Labels for Person Re-Identification Guile Wu S. Gong OOD 99 31 0 07 Jun 2020
Cheetah: Optimizing and Accelerating Homomorphic Encryption for Private Inference Brandon Reagen Wooseok Choi Yeongil Ko Vincent T. Lee Gu-Yeon Wei Hsien-Hsin S. Lee David Brooks 69 16 0 31 May 2020
Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins? Jeremy Georges-Filteau Elisa Cirillo SyDa AI4CE 104 17 0 27 May 2020
An Overview of Privacy in Machine Learning Emiliano De Cristofaro SILM 81 87 0 18 May 2020
Private Stochastic Convex Optimization: Optimal Rates in Linear Time Vitaly Feldman Tomer Koren Kunal Talwar 85 211 0 10 May 2020
Differentially Private Federated Learning with Laplacian Smoothing Zhicong Liang Bao Wang Quanquan Gu Stanley Osher Yuan Yao FedML 81 8 0 01 May 2020
Privacy in Deep Learning: A Survey Fatemehsadat Mirshghallah Mohammadkazem Taram Praneeth Vepakomma Abhishek Singh Ramesh Raskar H. Esmaeilzadeh FedML 129 139 0 25 Apr 2020
A Review of Privacy-preserving Federated Learning for the Internet-of-Things Christopher Briggs Zhong Fan Péter András 135 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 134 49 0 23 Apr 2020
Not All Features Are Equal: Discovering Essential Features for Preserving Prediction Privacy Fatemehsadat Mireshghallah Mohammadkazem Taram A. Jalali Ahmed T. Elthakeb Dean Tullsen H. Esmaeilzadeh 72 12 0 26 Mar 2020
Systematic Evaluation of Privacy Risks of Machine Learning Models Liwei Song Prateek Mittal MIACV 399 378 0 24 Mar 2020
The Cost of Privacy in Asynchronous Differentially-Private Machine Learning F. Farokhi Nan Wu David Smith M. Kâafar FedML 40 0 0 18 Mar 2020
Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion Qinqing Zheng Jinshuo Dong Qi Long Weijie J. Su FedML 70 23 0 10 Mar 2020
Differential Privacy at Risk: Bridging Randomness and Privacy Budget Ashish Dandekar D. Basu S. Bressan 109 8 0 02 Mar 2020
Differentially Private Deep Learning with Smooth Sensitivity Lichao Sun Yingbo Zhou Philip S. Yu Caiming Xiong FedML 70 10 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 154 101 0 26 Feb 2020
Preventing Imitation Learning with Adversarial Policy Ensembles Albert Zhan Stas Tiomkin Pieter Abbeel 33 3 0 31 Jan 2020
DP-CGAN: Differentially Private Synthetic Data and Label Generation Reihaneh Torkzadehmahani Peter Kairouz B. Paten SyDa 79 239 0 27 Jan 2020
A Federated Deep Learning Framework for Privacy Preservation and Communication Efficiency Tien-Dung Cao Tram Truong-Huu H. Tran K. Tran FedML 36 28 0 22 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 82 40 0 16 Jan 2020
Artificial Intelligence for Social Good: A Survey Zheyuan Ryan Shi Claire Wang Fei Fang AI4TS 113 81 0 07 Jan 2020
Assessing differentially private deep learning with Membership Inference Daniel Bernau Philip-William Grassal J. Robl Florian Kerschbaum MIACV FedML 98 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 92 169 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 93 37 0 19 Dec 2019
Learning to Prevent Leakage: Privacy-Preserving Inference in the Mobile Cloud Shuang Zhang Liyao Xiang Congcong Li Yixuan Wang Quanshi Zhang Zeyu Liu Yue Liu FedML 36 1 0 18 Dec 2019
Privacy-Preserving Blockchain Based Federated Learning with Differential Data Sharing Anudit Nagar 51 21 0 10 Dec 2019
Differentially Private Synthetic Mixed-Type Data Generation For Unsupervised Learning U. Tantipongpipat Chris Waites Digvijay Boob Amaresh Ankit Siva Rachel Cummings SyDa 97 31 0 06 Dec 2019
TeaNet: universal neural network interatomic potential inspired by iterative electronic relaxations So Takamoto S. Izumi Ju Li GNN 65 80 0 02 Dec 2019
Deep Learning with Gaussian Differential Privacy Zhiqi Bu Jinshuo Dong Qi Long Weijie J. Su FedML 103 209 0 26 Nov 2019
PAC learning with stable and private predictions Y. Dagan Vitaly Feldman 67 13 0 24 Nov 2019
Federated Learning with Bayesian Differential Privacy Aleksei Triastcyn Boi Faltings FedML 83 180 0 22 Nov 2019
Privacy Leakage Avoidance with Switching Ensembles R. Izmailov Peter Lin Chris Mesterharm S. Basu 61 2 0 18 Nov 2019
Generative Models for Effective ML on Private, Decentralized Datasets S. Augenstein H. B. McMahan Daniel Ramage Swaroop Indra Ramaswamy Peter Kairouz Mingqing Chen Rajiv Mathews Blaise Agüera y Arcas SyDa 87 183 0 15 Nov 2019
Towards a General Model of Knowledge for Facial Analysis by Multi-Source Transfer Learning Valentin Vielzeuf Alexis Lechervy S. Pateux F. Jurie CVBM 65 5 0 08 Nov 2019
What is Fair? Exploring Pareto-Efficiency for Fairness Constrained Classifiers Ananth Balashankar Alyssa Lees Chris Welty L. Subramanian 85 21 0 30 Oct 2019
Distribution Density, Tails, and Outliers in Machine Learning: Metrics and Applications Nicholas Carlini Ulfar Erlingsson Nicolas Papernot OOD OODD 71 61 0 29 Oct 2019
Secure Evaluation of Quantized Neural Networks Anders Dalskov Daniel E. Escudero Marcel Keller 109 143 0 28 Oct 2019
Limits of Private Learning with Access to Public Data N. Alon Raef Bassily Shay Moran 70 49 0 25 Oct 2019
Towards Distributed Privacy-Preserving Prediction Lingjuan Lyu Yee Wei Law K. S. Ng Shibei Xue Jun Zhao Mengmeng Yang Lei Liu FedML 34 4 0 25 Oct 2019
Federated Generative Privacy Aleksei Triastcyn Boi Faltings FedML 72 63 0 18 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 67 94 0 18 Oct 2019
Not Just Cloud Privacy: Protecting Client Privacy in Teacher-Student Learning Lichao Sun Ji Wang Philip S. Yu Lifang He 15 0 0 17 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 53 54 0 12 Oct 2019
PPGAN: Privacy-preserving Generative Adversarial Network Yi Liu Jialiang Peng James Jianqiao Yu Yi Wu 73 70 0 04 Oct 2019
Alleviating Privacy Attacks via Causal Learning Shruti Tople Amit Sharma A. Nori MIACV OOD 98 32 0 27 Sep 2019
Synthetic Data for Deep Learning Sergey I. Nikolenko 149 358 0 25 Sep 2019
A Knowledge Transfer Framework for Differentially Private Sparse Learning Lingxiao Wang Quanquan Gu FedML 47 14 0 13 Sep 2019
Duet: An Expressive Higher-order Language and Linear Type System for Statically Enforcing Differential Privacy Joseph P. Near David Darais Chiké Abuah Tim Stevens Pranav Gaddamadugu ... Neel Somani Mu Zhang Nikhil Sharma Alex Shan Basel Alomair 74 38 0 05 Sep 2019
Key Protected Classification for Collaborative Learning Mert Bulent Sariyildiz R. G. Cinbis Erman Ayday 49 10 0 27 Aug 2019
Federated Learning: Challenges, Methods, and Future Directions Tian Li Anit Kumar Sahu Ameet Talwalkar Virginia Smith FedML 179 4,581 0 21 Aug 2019