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In differential privacy (DP), a challenging problem is to generate synthetic datasets that efficiently capture the useful information in the private data.
Network flows
Ravindra K Ahuja, Thomas L Magnanti, and James B Orlin · 1988
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Revealing information while preserving privacy
Irit Dinur and Kobbi Nissim · 2003
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Differential privacy
Cynthia Dwork · 2006
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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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The price of privacy and the limits of LP decoding
Cynthia Dwork, Frank McSherry, and Kunal Talwar · 2007
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Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
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A learning theory approach to non-interactive database privacy
Avrim Blum, Katrina Ligett, and Aaron Roth · 2008
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New efficient attacks on statistical disclosure control mechanisms
C Dwork and S Yekhanin · 2008
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On the complexity of differentially private data release: efficient algorithms and hardness results
C Dwork, M Naor, O Reingold, G.N Rothblum, and S Vadhan · 2009
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On the complexity of differentially private data release: efficient algorithms and hardness results
Cynthia Dwork, Moni Naor, Omer Reingold, Guy N Rothblum, and Salil Vadhan · 2009
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UCI machine learning repository, 2010
A. Asuncion and D.J. Newman · 2010
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Boosting and differential privacy
C Dwork, G Rothblum, and S Vadhan · 2010
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Optimizing linear counting queries under differential privacy
Chao Li, Michael Hay, Vibhor Rastogi, Gerome Miklau, and Andrew McGregor · 2010
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Differential privacy via wavelet transforms
Xiaokui Xiao, Guozhang Wang, and Johannes Gehrke · 2010
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Differentially private data cubes: optimizing noise sources and consistency
Bolin Ding, Marianne Winslett, Jiawei Han, and Zhenhui Li · 2011
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No free lunch in data privacy
Daniel Kifer and Ashwin Machanavajjhala · 2011
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Pcps and the hardness of generating private synthetic data
Jonathan Ullman and Salil Vadhan · 2011
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The multiplicative weights update method: a meta-algorithm and applications
Sanjeev Arora, Elad Hazan, and Satyen Kale · 2012
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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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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Privbasis: Frequent itemset mining with differential privacy
Ninghui Li, Wahbeh Qardaji, Dong Su, and Jianneng Cao · 2012
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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Understanding the sparse vector technique for differential privacy
Min Lyu, Dong Su, and Ninghui Li · 2017
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The complexity of differential privacy
Salil Vadhan · 2017
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Privsuper: A superset-first approach to frequent itemset mining under differential privacy
Ning Wang, Xiaokui Xiao, Yin Yang, Zhenjie Zhang, Yu Gu, and Ge Yu · 2017
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Privbayes: Private data release via bayesian networks
Jun Zhang, Graham Cormode, Cecilia M Procopiuc, Divesh Srivastava, and Xiaokui Xiao · 2017
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Privacy preserving synthetic data release using deep learning
Nazmiye Ceren Abay, Yan Zhou, Murat Kantarcioglu, Bhavani Thuraisingham, and Latanya Sweeney · 2018
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The us census bureau adopts differential privacy
John M Abowd · 2018
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Dual query: Practical private query release for high dimensional data
Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, and Zhiwei Steven Wu · 2014
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Top-k frequent itemsets via differentially private fp-trees
Jaewoo Lee and Christopher W Clifton · 2014
Cited alongside, same era.
Priview: practical differentially private release of marginal contingency tables
Wahbeh Qardaji, Weining Yang, and Ninghui Li · 2014
Cited alongside, same era.
Differentially private high-dimensional data publication via sampling-based inference
Rui Chen, Qian Xiao, Yu Zhang, and Jianliang Xu · 2015
Cited alongside, same era.
Maximum likelihood postprocessing for differential privacy under consistency constraints
Jaewoo Lee, Yue Wang, and Daniel Kifer · 2015
Cited alongside, same era.
Deep learning with differential privacy
Martín Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Cited alongside, same era.
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Towards practical differential privacy for sql queries
Noah Johnson, Joseph P Near, and Dawn Song · 2018
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2018 differential privacy synthetic data challenge
NIST · 2018
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Differentially private releasing via deep generative model
Xinyang Zhang, Shouling Ji, and Ting Wang · 2018
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Linear queries estimation with local differential privacy
Raef Bassily · 2019
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Privacy-preserving generative deep neural networks support clinical data sharing
Brett K Beaulieu-Jones, Zhiwei Steven Wu, Chris Williams, Ran Lee, Sanjeev P Bhavnani, James Brian Byrd, and Casey S Greene · 2019
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Differentially private generative adversarial networks for time series, continuous, and discrete open data
Lorenzo Frigerio, Anderson Santana de Oliveira, Laurent Gomez, and Patrick Duverger · 2019
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Graphical-model based estimation and inference for differential privacy
Ryan Mckenna, Daniel Sheldon, and Gerome Miklau · 2019
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Differentially private mixed-type data generation for unsupervised learning
Uthaipon Tantipongpipat, Chris Waites, Digvijay Boob, Amaresh Ankit Siva, and Rachel Cummings · 2019
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Linkedin’s audience engagements api: A privacy preserving data analytics system at scale
Ryan Rogers, Subbu Subramaniam, Sean Peng, David Durfee, Seunghyun Lee, Santosh Kumar Kancha, Shraddha Sahay, and Parvez Ahammad · 2020
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Locally differentially private frequency estimation with consistency
Tianhao Wang, Milan Lopuhaä-Zwakenberg, Zitao Li, Boris Skoric, and Ninghui Li · 2020
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