Fetching the paper…
Reading the bibliography…
We demonstrate that it is possible to train large recurrent language models with user-level differential privacy guarantees with only a negligible cost in predictive accuracy.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
Earlier work this paper cites.
Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D. Sarwate · 2011
Earlier work this paper cites.
A firm foundation for private data analysis
Cynthia Dwork · 2011
Earlier work this paper cites.
Differentially private continual monitoring of heavy hitters from distributed streams
T-H Hubert Chan, Mingfei Li, Elaine Shi, and Wenchang Xu · 2012
Earlier work this paper cites.
LSTM neural networks for language modeling
Martin Sundermeyer, Ralf Schlüter, and Hermann Ney · 2012
Earlier work this paper cites.
Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
Earlier work this paper cites.
The Algorithmic Foundations of Differential Privacy
Cynthia Dwork and Aaron Roth · 2014
Cited alongside, same era.
Conversational contextual cues: The case of personalization and history for response ranking
Rami Al-Rfou, Marc Pickett, Javier Snaider, Yun-Hsuan Sung, Brian Strope, and Ray Kurzweil · 2016
Cited alongside, same era.
A theoretically grounded application of dropout in recurrent neural networks
Yarin Gal and Zoubin Ghahramani · 2016
Cited alongside, same era.
Improving neural language models with a continuous cache
Edouard Grave, Armand Joulin, and Nicolas Usunier · 2016
Cited alongside, same era.
Federated learning of deep networks using model averaging, 2016
H. Brendan McMahan, Eider Moore, Daniel Ramage, and Blaise Aguera y Arcas · 2016
Cited alongside, same era.
Practical locally private heavy hitters
R. Bassily, K. Nissim, U. Stemmer, and A. Thakurta · 2017
Closest in time.
Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 2017
Closest in time.
Using the output embedding to improve language models
Ofir Press and Lior Wolf · 2017
Closest in time.
Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Closest in time.
Bolt-on differential privacy for scalable stochastic gradient descent-based analytics
Xi Wu, Fengan Li, Arun Kumar, Kamalika Chaudhuri, Somesh Jha, and Jeffrey F. Naughton · 2017
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher · 2016
Cited alongside, same era.
Reddit comments dataset
Reddit Comments Dataset · 2016
Cited alongside, same era.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang
Cited in the paper.
Source code for “deep learning with differential privacy”
Martin Abadi, Andy Chu, Ian Goodfellow, Brendan McMahan, Ilya Mironov, Kunal Talwar, Li Zhang, and Xin Pan
Cited in the paper.
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2017
Closest in time.
The Secret Sharer: Measuring Unintended Neural Network Memorization & Extracting Secrets
N. Carlini, C. Liu, J. Kos, Ú. Erlingsson, and D. Song · 2018
Closest in time.