Privacy-Preserving Personal Model Training

v1v2v3 (latest)

Privacy-Preserving Personal Model Training

1 March 2017

S. S. Rodríguez

Liang Wang

Jianxin R. Zhao

Richard Mortier

Hamed Haddadi

ArXiv (abs)PDF HTML

Papers citing "Privacy-Preserving Personal Model Training"

10 / 10 papers shown

Title
Membership Inference Attacks against Machine Learning Models Reza Shokri M. Stronati Congzheng Song Vitaly Shmatikov SLR MIALM MIACV 261 4,135 0 18 Oct 2016
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data Nicolas Papernot Martín Abadi Ulfar Erlingsson Ian Goodfellow Kunal Talwar 77 1,020 0 18 Oct 2016
Deep Learning with Differential Privacy Martín Abadi Andy Chu Ian Goodfellow H. B. McMahan Ilya Mironov Kunal Talwar Li Zhang FedML SyDa 213 6,130 0 01 Jul 2016
Communication-Efficient Learning of Deep Networks from Decentralized Data H. B. McMahan Eider Moore Daniel Ramage S. Hampson Blaise Agüera y Arcas FedML 406 17,486 0 17 Feb 2016
Learning Privately from Multiparty Data Jihun Hamm Yingjun Cao M. Belkin FedML 48 165 0 10 Feb 2016
Fast k-NN search Ville Hyvönen Teemu Pitkänen S. Tasoulis Elias Jääsaari Risto Tuomainen Liang Wang J. Corander Teemu Roos 18 11 0 23 Sep 2015
MLlib: Machine Learning in Apache Spark Xiangrui Meng Joseph K. Bradley Burak Yavuz Evan R. Sparks Shivaram Venkataraman ... Reynold Xin Michael Franklin R. Zadeh Matei A. Zaharia Ameet Talwalkar 58 1,784 0 26 May 2015
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification Kaiming He Xinming Zhang Shaoqing Ren Jian Sun VLM 326 18,625 0 06 Feb 2015
Poisoning Attacks against Support Vector Machines Battista Biggio B. Nelson Pavel Laskov AAML 112 1,590 0 27 Jun 2012
Differentially Private Empirical Risk Minimization Kamalika Chaudhuri C. Monteleoni Anand D. Sarwate 134 1,487 0 01 Dec 2009

We use cookies and other tracking technologies to improve your browsing experience on our website, to show you personalized content and targeted ads, to analyze our website traffic, and to understand where our visitors are coming from. See our policy.