Fetching the paper…
Reading the bibliography…
Crowdsourced data used in machine learning services might carry sensitive information about attributes that users do not want to share.
Divergence measures based on the Shannon entropy
Jianhua Lin · 1991
Earlier work this paper cites.
A new metric for probability distributions
Dominik Maria Endres and Johannes E Schindelin · 2003
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.
Resolving individuals contributing trace amounts of dna to highly complex mixtures using high-density snp genotyping microarrays
Nils Homer, Szabolcs Szelinger, Margot Redman, David Duggan, Waibhav Tembe, Jill Muehling, John V Pearson, Dietrich A Stephan, Stanley F Nelson, and David W Craig · 2008
Earlier work this paper cites.
The exponential complexity of satisfiability problems
Chris Calabro · 2009
Earlier work this paper cites.
Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
Earlier work this paper cites.
Sensorsift: balancing sensor data privacy and utility in automated face understanding
Miro Enev, Jaeyeon Jung, Liefeng Bo, Xiaofeng Ren, and Tadayoshi Kohno · 2012
Earlier work this paper cites.
Discriminately decreasing discriminability with learned image filters
Jacob Whitehill and Javier Movellan · 2012
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
Earlier work this paper cites.
Censoring representations with an adversary
Harrison Edwards and Amos Storkey · 2015
Earlier work this paper cites.
Mlaas: Machine learning as a service
Mauro Ribeiro, Katarina Grolinger, and Miriam AM Capretz · 2015
Earlier work this paper cites.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Earlier work this paper cites.
Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
Earlier work this paper cites.
Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy
Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin Lauter, Michael Naehrig, and John Wernsing · 2016
Cited alongside, same era.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, Nati Srebro, et al · 2016
Cited alongside, same era.
Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martín Abadi, Ulfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 2016
Cited alongside, same era.
Sergey Zagoruyko and Nikos Komodakis · 2016
Cited alongside, same era.
UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
Cited alongside, same era.
Towards robust and privacy-preserving text representations
Yitong Li, Timothy Baldwin, and Trevor Cohn · 2018
Later among the works it cites.
Deep private-feature extraction
Seyed Ali Osia, Ali Taheri, Ali Shahin Shamsabadi, Kleomenis Katevas, Hamed Haddadi, and Hamid R Rabiee · 2018
Later among the works it cites.
Not just privacy: Improving performance of private deep learning in mobile cloud
Ji Wang, Jianguo Zhang, Weidong Bao, Xiaomin Zhu, Bokai Cao, and Philip S Yu · 2018
Later among the works it cites.
Towards privacy-preserving visual recognition via adversarial training: A pilot study
Zhenyu Wu, Zhangyang Wang, Zhaowen Wang, and Hailin Jin · 2018
Later among the works it cites.
Privacy risk in machine learning: Analyzing the connection to overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Minimax filter: Learning to preserve privacy from inference attacks
Jihun Hamm · 2017
Cited alongside, same era.
Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Cited alongside, same era.
Privacy-utility tradeoffs under constrained data release mechanisms
Ye Wang, Yuksel Ozan Basciftci, and Prakash Ishwar · 2017
Cited alongside, same era.
Age progression/regression by conditional adversarial autoencoder
Zhifei Zhang, Yang Song, and Hairong Qi · 2017
Cited alongside, same era.
Faster cryptonets: Leveraging sparsity for real-world encrypted inference
Edward Chou, Josh Beal, Daniel Levy, Serena Yeung, Albert Haque, and Li Fei-Fei · 2018
Cited alongside, same era.
The limit points of (optimistic) gradient descent in min-max optimization
Constantinos Daskalakis and Ioannis Panageas · 2018
Cited alongside, same era.
General Data Protection Regulation
EU · 2018
Cited alongside, same era.
Adversarially learned representations for information obfuscation and inference
Martin Bertran, Natalia Martinez, Afroditi Papadaki, Qiang Qiu, Miguel Rodrigues, Galen Reeves, and Guillermo Sapiro · 2019
Closest in time.
Mitigating information leakage in image representations: A maximum entropy approach
Proteek Chandan Roy and Vishnu Naresh Boddeti · 2019
Closest in time.
Privacy-preserving adversarial representation learning in asr: Reality or illusion?
Brij Mohan Lal Srivastava, Aurélien Bellet, Marc Tommasi, and Emmanuel Vincent · 2019
Closest in time.
Disparate vulnerability: On the unfairness of privacy attacks against machine learning
Mohammad Yaghini, Bogdan Kulynych, and Carmela Troncoso · 2019
Closest in time.
Inherent tradeoffs in learning fair representations
Han Zhao and Geoffrey J Gordon · 2019
Closest in time.
On learning invariant representations for domain adaptation
Han Zhao, Remi Tachet Des Combes, Kun Zhang, and Geoffrey Gordon · 2019
Closest in time.
A hybrid deep learning architecture for privacy-preserving mobile analytics
Seyed Ali Osia, Ali Shahin Shamsabadi, Sina Sajadmanesh, Ali Taheri, Kleomenis Katevas, Hamid R Rabiee, Nicholas D Lane, and Hamed Haddadi · 2020
Closest in time.