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Over the past few years, providers such as Google, Microsoft, and Amazon have started to provide customers with access to software interfaces allowing them to easily embed machine learning tasks into their applications.
Towards a methodology for statistical disclosure control
T. Dalenius · 1977
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Privacy preserving data mining
Y. Lindell and B. Pinkas · 2000
Earlier work this paper cites.
Privacy-preserving multivariate statistical analysis: Linear regression and classification
W. Du, Y. S. Han, and S. Chen · 2004
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Differential privacy: A survey of results
C. Dwork · 2008
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Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays
N. Homer, S. Szelinger, M. Redman, D. Duggan, W. Tembe, J. Muehling, J. V. Pearson, D. A. Stephan, S. F. Nelson, and D. W. Craig · 2008
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On the difficulties of disclosure prevention in statistical databases or the case for differential privacy
C. Dwork and M. Naor · 2010
Earlier work this paper cites.
The Algorithmic Foundations of Differential Privacy
C. Dwork and A. Roth · 2013
Earlier work this paper cites.
Ml confidential: Machine learning on encrypted data
T. Graepel, K. Lauter, and M. Naehrig · 2013
Earlier work this paper cites.
Privacy-preserving ridge regression on hundreds of millions of records
V. Nikolaenko, U. Weinsberg, S. Ioannidis, M. Joye, D. Boneh, and N. Taft · 2013
Earlier work this paper cites.
Private Predictive Analysis on Encrypted Medical Data
J. W. Bos, K. Lauter, and M. Naehrig · 2014
Earlier work this paper cites.
Machine learning classification over encrypted data
R. Bost, R. A. Popa, S. Tu, and S. Goldwasser · 2014
Earlier work this paper cites.
Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing
M. Fredrikson, E. Lantz, S. Jha, S. Lin, D. Page, and T. Ristenpart · 2014
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers
G. Ateniese, L. V. Mancini, A. Spognardi, A. Villani, D. Vitali, and G. Felici · 2015
Earlier work this paper cites.
Robust traceability from trace amounts
C. Dwork, A. Smith, T. Steinke, J. Ullman, and S. Vadhan · 2015
Earlier work this paper cites.
Model inversion attacks that exploit confidence information and basic countermeasures
M. Fredrikson, S. Jha, and T. Ristenpart · 2015
Earlier work this paper cites.
Privacy-preserving deep learning
R. Shokri and V. Shmatikov · 2015
Earlier work this paper cites.
Deep learning with differential privacy
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang · 2016
Earlier work this paper cites.
Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy
N. Dowlin, R. Gilad-Bachrach, K. Laine, K. Lauter, M. Naehrig, and J. Wernsing · 2016
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Oblivious multi-party machine learning on trusted processors
O. Ohrimenko, F. Schuster, C. Fournet, A. Mehta, S. Nowozin, K. Vaswani, and M. Costa · 2016
Earlier work this paper cites.
Stealing machine learning models via prediction APIs
F. Tramèr, F. Zhang, A. Juels, M. K. Reiter, and T. Ristenpart · 2016
Cited alongside, same era.
Google DeepMind will use machine learning to spot eye diseases early
J. Vincent · 2016
Cited alongside, same era.
On the protection of private information in machine learning systems: Two recent approches
M. Abadi, U. Erlingsson, I. Goodfellow, H. B. McMahan, I. Mironov, N. Papernot, K. Talwar, and L. Zhang · 2017
Cited alongside, same era.
Privacy-preserving deep learning: Revisited and Enhanced
Y. Aono, T. Hayashi, L. Wang, S. Moriai, et al · 2017
Cited alongside, same era.
Practical secure aggregation for privacy-preserving machine learning
K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth · 2017
Cited alongside, same era.
Differentially private federated learning: A client level perspective
Mlcapsule: Guarded offline deployment of machine learning as a service
L. Hanzlik, Y. Zhang, K. Grosse, A. Salem, M. Augustin, M. Backes, and M. Fritz · 2018
Later among the works it cites.
Chiron: Privacy-preserving machine learning as a service
T. Hunt, C. Song, R. Shokri, V. Shmatikov, and E. Witchel · 2018
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Efficient deep learning on multi-source private data
N. Hynes, R. Cheng, and D. Song · 2018
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Learning differentially private language models without losing accuracy
H. B. McMahan, D. Ramage, K. Talwar, and L. Zhang · 2018
Later among the works it cites.
Towards reverse-engineering black-box neural networks
S. J. Oh, M. Augustin, M. Fritz, and B. Schiele · 2018
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R. C. Geyer, T. Klein, and M. Nabi · 2017
Cited alongside, same era.
Explained: Neural networks
L. Hardesty · 2017
Cited alongside, same era.
Deep models under the GAN: information leakage from collaborative deep learning
B. Hitaj, G. Ateniese, and F. Pérez-Cruz · 2017
Cited alongside, same era.
Oblivious neural network predictions via minionn transformations
J. Liu, M. Juuti, Y. Lu, and N. Asokan · 2017
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
H. B. McMahan, E. Moore, D. Ramage, S. Hampson, et al · 2017
Cited alongside, same era.
Secureml: A system for scalable privacy-preserving machine learning
P. Mohassel and Y. Zhang · 2017
Cited alongside, same era.
Differential privacy: A primer for a non-technical audience
K. Nissim, T. Steinke, A. Wood, M. Altman, A. Bembenek, M. Bun, M. Gaboardi, D. R. O’Brien, and S. Vadhan · 2017
Cited alongside, same era.
Sok: Security and privacy in machine learning
N. Papernot, P. McDaniel, A. Sinha, and M. P. Wellman · 2018
Later among the works it cites.
Scalable private learning with pate
N. Papernot, S. Song, I. Mironov, A. Raghunathan, K. Talwar, and Ú. Erlingsson · 2018
Later among the works it cites.
Knock Knock, Who’s There? Membership Inference on Aggregate Location Data
A. Pyrgelis, C. Troncoso, and E. De Cristofaro · 2018
Later among the works it cites.
Towards demystifying membership inference attacks
S. Truex, L. Liu, M. E. Gursoy, L. Yu, and W. Wei · 2018
Later among the works it cites.
Stealing hyperparameters in machine learning
B. Wang and N. Z. Gong · 2018
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Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting
S. Yeom, I. Giacomelli, M. Fredrikson, and S. Jha · 2018
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Towards Formalizing the GDPR’s Notion of Singling Out
A. Cohen and K. Nissim · 2019
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Logan: Membership inference attacks against generative models
J. Hayes, L. Melis, G. Danezis, and E. De Cristofaro · 2019
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Evaluating Differentially Private Machine Learning in Practice
B. Jayaraman and D. Evans · 2019
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Exploiting unintended feature leakage in collaborative learning
L. Melis, C. Song, E. De Cristofaro, and V. Shmatikov · 2019
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Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning
M. Nasr, R. Shokri, and A. Houmansadr · 2019
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Knockoff nets: Stealing functionality of black-box models
T. Orekondy, B. Schiele, and M. Fritz · 2019
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ML-Leaks: Model and data independent membership inference attacks and defenses on machine learning models
A. Salem, Y. Zhang, M. Humbert, P. Berrang, M. Fritz, and M. Backes · 2019
Later among the works it cites.
SLALOM: Fast, verifiable and private execution of neural networks in trusted hardware
F. Tramer and D. Boneh · 2019
Later among the works it cites.