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Machine learning practitioners frequently seek to leverage the most informative available data, without violating the data owner's privacy, when building predictive models.
Modeling tabular data using conditional GAN
Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni · 1907
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
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Differential privacy: A survey of results
Cynthia Dwork, Agrawal M., Du D., Duan Z., and Li A · 2008
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A multiplicative weights mechanism for privacy-preserving data analysis
Moritz Hardt and Guy N Rothblum · 2010
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Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate · 2011
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A simple and practical algorithm for differentially private data release
Moritz Hardt, Katrina Ligett, and Frank McSherry · 2012
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Jaideep Vaidya, Basit Shafiq, Anirban Basu, and Yuan Hong · 2013
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Differential privacy and machine learning: a survey and review
Zhanglong Ji, Zachary C Lipton, and Charles Elkan · 2014
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Privacy preserving rbf kernel support vector machine
Haoran Li, Li Xiong, Lucila Ohno-Machado, and Xiaoqian Jiang · 2014
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Private approximations of the 2nd-moment matrix using existing techniques in linear regression
Or Sheffet · 2015
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Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
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On the differential privacy of bayesian inference
Zuhe Zhang, Benjamin Rubinstein, and Christos Dimitrakakis · 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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Principled evaluation of differentially private algorithms using dpbench
Michael Hay, Ashwin Machanavajjhala, Gerome Miklau, Yan Chen, and Dan Zhang · 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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Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Collecting telemetry data privately
Bolin Ding, Janardhan Kulkarni, and Sergey Yekhanin · 2017
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Xinyang Zhang, Shouling Ji, and Ting Wang · 2018
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An algorithmic framework for differentially private data analysis on trusted processors
Joshua Allen, Bolin Ding, Janardhan Kulkarni, Harsha Nori, Olga Ohrimenko, and Sergey Yekhanin · 2019
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Gan-leaks: A taxonomy of membership inference attacks against gans
Dingfan Chen, Ning Yu, Yang Zhang, and Mario Fritz · 2019
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Logan: Membership inference attacks against generative models
Jamie Hayes, Luca Melis, George Danezis, and Emiliano De Cristofaro · 2019
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Stelios Doudalis, Ios Kotsogiannis, Samuel Haney, Ashwin Machanavajjhala, and Sharad Mehrotra · 2017
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UCI machine learning repository
Dheeru Dua and Casey Graff · 2017
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Briland Hitaj, Giuseppe Ateniese, and Fernando Perez-Cruz · 2017
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Differential privacy in the wild: A tutorial on current practices & open challenges
Ashwin Machanavajjhala, Xi He, and Michael Hay · 2017
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Preserving differential privacy in convolutional deep belief networks
NhatHai Phan, Xintao Wu, and Dejing Dou · 2017
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Privacy amplification by iteration
Vitaly Feldman, Ilya Mironov, Kunal Talwar, and Abhradeep Thakurta · 2018
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Are gans created equal? a large-scale study
Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, and Olivier Bousquet · 2018
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Evaluating differentially private machine learning in practice
Bargav Jayaraman and David Evans · 2019
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Private selection from private candidates
Jingcheng Liu and Kunal Talwar · 2019
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Protecting gans against privacy attacks by preventing overfitting
Sumit Mukherjee, Yixi Xu, Anusua Trivedi, and Juan Lavista Ferres · 2019
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Differential privacy and census data: Implications for social and economic research
Steven Ruggles, Catherine Fitch, Diana Magnuson, and Jonathan Schroeder · 2019
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Dp-cgan: Differentially private synthetic data and label generation
Reihaneh Torkzadehmahani, Peter Kairouz, and Benedict Paten · 2019
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Differential privacy preservation in deep learning: Challenges, opportunities and solutions
Jingwen Zhao, Yunfang Chen, and Wei Zhang · 2019
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Christian Arnold and Marcel Neunhoeffer · 2020
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IBM differential privacy library
diffprivlib · 2020
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Autogan-based dimension reduction for privacy preservation
Hung Nguyen, Di Zhuang, Pei-Yuan Wu, and Morris Chang · 2020
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New oracle-efficient algorithms for private synthetic data release
Giuseppe Vietri, Grace Tian, Mark Bun, Thomas Steinke, and Zhiwei Steven Wu · 2020
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