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When training a machine learning model with differential privacy, one sets a privacy budget.
Privacy in e-commerce: Stated preferences vs. actual behavior
Bettina Berendt, Oliver Günther, and Sarah Spiekermann · 2005
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Privacy practices of internet users: Self-reports versus observed behavior
Carlos Jensen, Colin Potts, and Christian Jensen · 2005
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Differential privacy: A survey of results
Cynthia Dwork · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Bounds on the sample complexity for private learning and private data release
Amos Beimel, Shiva Prasad Kasiviswanathan, and Kobbi Nissim · 2010
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MNIST handwritten digit database
Yann LeCun, Corinna Cortes, and C. J. Burges · 2010
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts · 2011
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Heterogeneous differential privacy
Mohammad Alaggan, Sébastien Gambs, and Anne-Marie Kermarrec · 2015
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A large annotated corpus for learning natural language inference
Samuel R Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning · 2015
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Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
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Conservative or liberal? personalized differential privacy
Zach Jorgensen, Ting Yu, and Graham Cormode · 2015
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martín Abadi, Ulfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 2016
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Partitioning-based mechanisms under personalized differential privacy
Haoran Li, Li Xiong, Zhanglong Ji, and Xiaoqian Jiang · 2017
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
Cited alongside, same era.
Rényi differential privacy
Ilya Mironov · 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.
Property testing for differential privacy
Anna C Gilbert and Audra McMillan · 2018
Cited alongside, same era.
Scalable private learning with pate
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Ulfar Erlingsson · 2018
Cited alongside, same era.
Towards effective differential privacy communication for users’ data sharing decision and comprehension
Aiping Xiong, Tianhao Wang, Ninghui Li, and Somesh Jha · 2020
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The secret revealer: Generative model-inversion attacks against deep neural networks
Yuheng Zhang, Ruoxi Jia, Hengzhi Pei, Wenxiao Wang, Bo Li, and Dawn Song · 2020
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" i need a better description": An investigation into user expectations for differential privacy
Rachel Cummings, Gabriel Kaptchuk, and Elissa M Redmiles · 2021
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Individual privacy accounting via a renyi filter
Vitaly Feldman and Tijana Zrnic · 2021
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Practical and private (deep) learning without sampling or shuffling
Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, and Zheng Xu · 2021
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Privacy risk in machine learning: Analyzing the connection to overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 2018
Cited alongside, same era.
Differentially private bagging: Improved utility and cheaper privacy than subsample-and-aggregate
James Jordon, Jinsung Yoon, and Mihaela van der Schaar · 2019
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova · 2019
Cited alongside, same era.
Rényi differential privacy of the sampled gaussian mechanism
Ilya Mironov, Kunal Talwar, and Li Zhang · 2019
Cited alongside, same era.
Hypothesis testing interpretations and renyi differential privacy
Borja Balle, Gilles Barthe, Marco Gaboardi, Justin Hsu, and Tetsuya Sato · 2020
Cited alongside, same era.
Utility-aware exponential mechanism for personalized differential privacy
Ben Niu, Yahong Chen, Boyang Wang, Jin Cao, and Fenghua Li · 2020
Cited alongside, same era.
Ben Niu, Yahong Chen, Boyang Wang, Zhibo Wang, Fenghua Li, and Jin Cao · 2021
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Privacy needs reflection: Conceptional design rationales for privacy-preserving explanation user interfaces
Peter Sörries, Claudia Müller-Birn, Katrin Glinka, Franziska Boenisch, Marian Margraf, Sabine Sayegh-Jodehl, and Matthias Rose · 2021
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Franziska Boenisch, Christopher Mühl, Roy Rinberg, Jannis Ihrig, and Adam Dziedzic · 2022
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Membership inference attacks from first principles
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, and Florian Tramer · 2022
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Daniel Franzen, Saskia Nuñez von Voigt, Peter Sörries, Florian Tschorsch, and Claudia Müller-Birn · 2022
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Reconstructing training data from trained neural networks
Niv Haim, Gal Vardi, Gilad Yehudai, Ohad Shamir, and Michal Irani · 2022
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Debugging differential privacy: A case study for privacy auditing
Florian Tramer, Andreas Terzis, Thomas Steinke, Shuang Song, Matthew Jagielski, and Nicholas Carlini · 2022
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Differentially private learning needs hidden state (or much faster convergence)
Jiayuan Ye and Reza Shokri · 2022
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Individual privacy accounting for differentially private stochastic gradient descent
Da Yu, Gautam Kamath, Janardhan Kulkarni, Tie-Yan Liu, Jian Yin, and Huishuai Zhang · 2022
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