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This explainer document aims to provide an overview of the current state of the rapidly expanding work on synthetic data technologies, with a particular focus on privacy.
Multiple imputation for survey nonresponse
Donald B Rubin · 1987
Earlier work this paper cites.
Discussion statistical disclosure limitation
Donald B Rubin · 1993
Earlier work this paper cites.
Statistical analysis of masked data
Roderick JA Little · 1993
Earlier work this paper cites.
Multiple imputation after 18+ years
Donald B Rubin · 1996
Earlier work this paper cites.
Does the Wake-sleep Algorithm Produce Good Density Estimators?
Brendan J Frey, Geoffrey E Hinton, and Peter Dayan · 1996
Earlier work this paper cites.
The infinite gaussian mixture model
Carl Edward Rasmussen · 1999
Earlier work this paper cites.
Disclosure limitation in longitudinal linked data
John M Abowd and Simon D Woodcock · 2001
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Agent-based modeling: Methods and techniques for simulating human systems
Eric Bonabeau · 2002
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Analysis of financial time series
Ruey S. Tsay · 2002
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Revealing information while preserving privacy
Irit Dinur and Kobbi Nissim · 2003
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Using cart to generate partially synthetic public use microdata
Jerome P Reiter · 2005
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Differential privacy
Cynthia Dwork · 2006
Earlier work this paper cites.
l-diversity: Privacy beyond k-anonymity
Ashwin Machanavajjhala, Daniel Kifer, Johannes Gehrke, and Muthuramakrishnan Venkitasubramaniam · 2007
Earlier work this paper cites.
t-closeness: Privacy beyond k-anonymity and l-diversity
Ninghui Li, Tiancheng Li, and Suresh Venkatasubramanian · 2007
Earlier work this paper cites.
Hiding the presence of individuals from shared databases
Mehmet Ercan Nergiz, Maurizio Atzori, and Chris Clifton · 2007
Earlier work this paper cites.
An introduction to copulas
Roger B Nelsen · 2007
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Restricted Boltzmann machines for collaborative filtering
Ruslan Salakhutdinov, Andriy Mnih, and Geoffrey Hinton · 2007
Earlier work this paper cites.
A general survey of privacy-preserving data mining models and algorithms
Charu C Aggarwal and S Yu Philip · 2008
Earlier work this paper cites.
A critique of k-anonymity and some of its enhancements
Josep Domingo-Ferrer and Vicenç Torra · 2008
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Robust de-anonymization of large sparse datasets
Arvind Narayanan and Vitaly Shmatikov · 2008
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.
Accounting for intruder uncertainty due to sampling when estimating identification disclosure risks in partially synthetic data
Jörg Drechsler and Jerome P Reiter · 2008
Earlier work this paper cites.
A novel bayes model: Hidden naive bayes
Liangxiao Jiang, Harry Zhang, and Zhihua Cai · 2008
Earlier work this paper cites.
Generating Synthetic Data to Match Data Mining Patterns
Joshua Eno and Craig Thompson · 2008
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How Protective Are Synthetic Data?
John M. Abowd and Lars Vilhuber · 2008
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Classifying without discriminating
Faisal Kamiran and Toon Calders · 2009
Earlier work this paper cites.
Differentially private recommender systems: Building privacy into the netflix prize contenders
Frank McSherry and Ilya Mironov · 2009
Earlier work this paper cites.
Accurate estimation of the degree distribution of private networks
Michael Hay, Chao Li, Gerome Miklau, and David Jensen · 2009
Earlier work this paper cites.
On the foundations of quantitative information flow
Geoffrey Smith · 2009
Earlier work this paper cites.
Global measures of data utility for microdata masked for disclosure limitation
Mi-Ja Woo, Jerome P Reiter, Anna Oganian, and Alan F Karr · 2009
Earlier work this paper cites.
Estimating risks of identification disclosure in partially synthetic data
Jerome P Reiter and Robin Mitra · 2009
Earlier work this paper cites.
Deep Boltzmann Machines
Ruslan Salakhutdinov and Geoffrey Hinton · 2009
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Probabilistic inference and differential privacy
Oliver Williams and Frank McSherry · 2010
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A statistical framework for differential privacy
Larry Wasserman and Shuheng Zhou · 2010
Earlier work this paper cites.
Statistical measurement of information leakage
Konstantinos Chatzikokolakis, Tom Chothia, and Apratim Guha · 2010
Earlier work this paper cites.
Using Support Vector Machines for Generating Synthetic Datasets
Jörg Drechsler · 2010
Earlier work this paper cites.
How much is enough? choosing ε \varepsilon for differential privacy
Jaewoo Lee and Chris Clifton · 2011
Earlier work this paper cites.
Pcps and the hardness of generating private synthetic data
Jonathan Ullman and Salil Vadhan · 2011
Earlier work this paper cites.
Sample complexity bounds for differentially private learning
Kamalika Chaudhuri and Daniel Hsu · 2011
Earlier work this paper cites.
The mnist database of handwritten digit images for machine learning research [best of the web]
Li Deng · 2012
Earlier work this paper cites.
Homophily and latent attribute inference: Inferring latent attributes of twitter users from neighbors
Faiyaz Al Zamal, Wendy Liu, and Derek Ruths · 2012
Earlier work this paper cites.
Beating randomized response on incoherent matrices
Moritz Hardt and Aaron Roth · 2012
Earlier work this paper cites.
Measuring information leakage using generalized gain functions
S Alvim M’rio, Kostas Chatzikokolakis, Catuscia Palamidessi, and Geoffrey Smith · 2012
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Stochastic gradient descent with differentially private updates
Shuang Song, Kamalika Chaudhuri, and Anand D Sarwate · 2013
Earlier work this paper cites.
Local privacy and statistical minimax rates
John C Duchi, Michael I Jordan, and Martin J Wainwright · 2013
Earlier work this paper cites.
Disclosure control using partially synthetic data for large-scale health surveys, with applications to cancors
Bronwyn Loong, Alan M Zaslavsky, Yulei He, and David P Harrington · 2013
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
Earlier work this paper cites.
Correlated network data publication via differential privacy
Rui Chen, Benjamin CM Fung, S Yu Philip, and Bipin C Desai · 2014
Earlier work this paper cites.
On the’semantics’ of differential privacy: A bayesian formulation
Shiva P Kasiviswanathan and Adam Smith · 2014
Earlier work this paper cites.
Differential privacy: An economic method for choosing epsilon
Justin Hsu, Marco Gaboardi, Andreas Haeberlen, Sanjeev Khanna, Arjun Narayan, Benjamin C Pierce, and Aaron Roth · 2014
Earlier work this paper cites.
Differentially private synthesization of multi-dimensional data using copula functions
Haoran Li, Li Xiong, and Xiaoqian Jiang · 2014
Earlier work this paper cites.
Conditional Generative Adversarial Nets
Mehdi Mirza and Simon Osindero · 2014
Earlier work this paper cites.
Certifying and removing disparate impact
Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian · 2015
Earlier work this paper cites.
Robust traceability from trace amounts
Cynthia Dwork, Adam Smith, Thomas Steinke, Jonathan Ullman, and Salil Vadhan · 2015
Earlier work this paper cites.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
Earlier work this paper cites.
Generative moment matching networks
Yujia Li, Kevin Swersky, and Richard S. Zemel · 2015
Earlier work this paper cites.
DRAW: A Recurrent Neural Network For Image Generation
Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, and Daan Wierstra · 2015
Earlier work this paper cites.
A causal framework for discovering and removing direct and indirect discrimination
Lu Zhang, Yongkai Wu, and Xintao Wu · 2016
Earlier work this paper cites.
Understanding data augmentation for classification: when to warp?
Sebastien C Wong, Adam Gatt, Victor Stamatescu, and Mark D McDonnell · 2016
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Semi-supervised learning with generative adversarial networks
Augustus Odena · 2016
Earlier work this paper cites.
Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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.
Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martín Abadi, Ulfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 2016
Earlier work this paper cites.
Statistical inference considered harmful, 2016
Frank McSherry · 2016
Earlier work this paper cites.
The Synthetic Data Vault
Neha Patki, Roy Wedge, and Kalyan Veeramachaneni · 2016
Earlier work this paper cites.
synthpop: Bespoke creation of synthetic data in r
Beata Nowok, Gillian M Raab, and Chris Dibben · 2016
Earlier work this paper cites.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
Earlier work this paper cites.
The case for process fairness in learning: Feature selection for fair decision making
Nina Grgic-Hlaca, Muhammad Bilal Zafar, Krishna P Gummadi, and Adrian Weller · 2016
Earlier work this paper cites.
Improved Techniques for Training GANs
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Cited alongside, same era.
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
Cited alongside, same era.
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Alec Radford, Luke Metz, and Soumith Chintala · 2016
Cited alongside, same era.
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
Cited alongside, same era.
Stochastic Adversarial Video Prediction
Alex X. Lee, Richard Zhang, Frederik Ebert, Pieter Abbeel, Chelsea Finn, and Sergey Levine · 2018
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Decomposing Motion and Content for Natural Video Sequence Prediction
Ruben Villegas, Jimei Yang, Seunghoon Hong, Xunyu Lin, and Honglak Lee · 2018
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Recycle-GAN: Unsupervised Video Retargeting
Aayush Bansal, Shugao Ma, Deva Ramanan, and Yaser Sheikh · 2018
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Hyeongwoo Kim, Pablo Garrido, Ayush Tewari, Weipeng Xu, Justus Thies, Matthias Nießner, Patrick Pérez, Christian Richardt, Michael Zollhöfer, and Christian Theobalt · 2018
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Privacy and synthetic datasets
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Elman Mansimov, Emilio Parisotto, Jimmy Lei Ba, and Ruslan Salakhutdinov · 2016
Cited alongside, same era.
Unsupervised Cross-Domain Image Generation
Yaniv Taigman, Adam Polyak, and Lior Wolf · 2016
Cited alongside, same era.
Coupled Generative Adversarial Networks
Ming-Yu Liu and Oncel Tuzel · 2016
Cited alongside, same era.
Generating Videos with Scene Dynamics
Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba · 2016
Cited alongside, same era.
Deep multi-scale video prediction beyond mean square error
Michael Mathieu, Camille Couprie, and Yann LeCun · 2016
Cited alongside, same era.
Real-valued (medical) time series generation with recurrent conditional gans
Cristóbal Esteban, Stephanie L Hyland, and Gunnar Rätsch · 2017
Cited alongside, same era.
Optimized pre-processing for discrimination prevention
Flavio Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, and Kush R Varshney · 2017
Cited alongside, same era.
Good semi-supervised learning that requires a bad gan
Zihang Dai, Zhilin Yang, Fan Yang, William W Cohen, and Ruslan Salakhutdinov · 2017
Cited alongside, same era.
Steven M Bellovin, Preetam K Dutta, and Nathan Reitinger · 2019
Later among the works it cites.
Synthetic data for deep learning
Sergey I Nikolenko · 2019
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An economic analysis of privacy protection and statistical accuracy as social choices
John M Abowd and Ian M Schmutte · 2019
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Damien Desfontaines and Balázs Pejó · 2019
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Evaluating differentially private machine learning in practice
Bargav Jayaraman and David Evans · 2019
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F-bleau: fast black-box leakage estimation
Giovanni Cherubin, Konstantinos Chatzikokolakis, and Catuscia Palamidessi · 2019
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Assessing privacy and quality of synthetic health data
Andrew Yale, Saloni Dash, Ritik Dutta, Isabelle Guyon, Adrien Pavao, and Kristin P Bennett · 2019
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Time-series generative adversarial networks
Jinsung Yoon, Daniel Jarrett, and Mihaela van der Schaar · 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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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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A survey on image data augmentation for deep learning
Connor Shorten and Taghi M Khoshgoftaar · 2019
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Modeling Tabular data using Conditional GAN
Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni · 2019
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Time Series Simulation by Conditional Generative Adversarial Net
Rao Fu, Jie Chen, Shutian Zeng, Yiping Zhuang, and Agus Sudjianto · 2019
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Deep Generative Models for Image Generation: A Practical Comparison Between Variational Autoencoders and Generative Adversarial Networks
Mohamed El-Kaddoury, Abdelhak Mahmoudi, and Mohammed Majid Himmi · 2019
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Image De-raining Using a Conditional Generative Adversarial Network
He Zhang, Vishwanath Sindagi, and Vishal M. Patel · 2019
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Large Scale GAN Training for High Fidelity Natural Image Synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
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MirrorGAN: Learning Text-to-image Generation by Redescription
Tingting Qiao, Jing Zhang, Duanqing Xu, and Dacheng Tao · 2019
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Chris Donahue, Julian McAuley, and Miller Puckette · 2019
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Talking Face Generation by Adversarially Disentangled Audio-Visual Representation
Hang Zhou, Yu Liu, Ziwei Liu, Ping Luo, and Xiaogang Wang · 2019
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Animating Face using Disentangled Audio Representations
Gaurav Mittal and Baoyuan Wang · 2019
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Conditional GAN with discriminative filter generation for text-to-video synthesis
Yogesh Balaji, Martin Renqiang Min, Bing Bai, Rama Chellappa, and Hans Peter Graf · 2019
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StoryGAN: A Sequential Conditional GAN for Story Visualization
Yitong Li, Zhe Gan, Yelong Shen, Jingjing Liu, Yu Cheng, Yuexin Wu, Lawrence Carin, David Carlson, and Jianfeng Gao · 2019
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Caroline Chan, Shiry Ginosar, Tinghui Zhou, and Alexei A. Efros · 2019
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Animating Arbitrary Objects via Deep Motion Transfer
Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci, and Nicu Sebe · 2019
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James Jordon, Alan Wilson, and Mihaela van der Schaar · 2020
Later among the works it cites.
Generating synthetic data in finance: opportunities, challenges and pitfalls
Samuel Assefa · 2020
Later among the works it cites.
Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2020
Later among the works it cites.
Factors associated with covid-19-related death using opensafely
Elizabeth J Williamson, Alex J Walker, Krishnan Bhaskaran, Seb Bacon, Chris Bates, Caroline E Morton, Helen J Curtis, Amir Mehrkar, David Evans, Peter Inglesby, et al · 2020
Later among the works it cites.
Disclosure avoidance for the 2020 census: An introduction, november 2021
US Census Bureau · 2020
Later among the works it cites.
Census bureau sets key parameters to protect privacy in 2020 census results, 2021
United States Census Bureau · 2020
Later among the works it cites.
How statistical noise is protecting your data privacy, 2020
Microsoft · 2020
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New privacy-protected facebook data for independent research on social media’s impact on democracy, 2020
Facebook Research · 2020
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Synthetic data–anonymisation groundhog day
Theresa Stadler, Bristena Oprisanu, and Carmela Troncoso · 2020
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Auditing differentially private machine learning: How private is private sgd?
Matthew Jagielski, Jonathan Ullman, and Alina Oprea · 2020
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Truly anonymous synthetic data - evolving legal definitions and technologies (part ii), 2020
MOSTLY AI · 2020
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Anonymization through data synthesis using generative adversarial networks (ads-gan)
Jinsung Yoon, Lydia N Drumright, and Mihaela Van Der Schaar · 2020
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Gan-leaks: A taxonomy of membership inference attacks against generative models
Dingfan Chen, Ning Yu, Yang Zhang, and Mario Fritz · 2020
Later among the works it cites.
Reliable fidelity and diversity metrics for generative models
Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh, Yunjey Choi, and Jaejun Yoo · 2020
Later among the works it cites.
Gender bias in neural natural language processing
Kaiji Lu, Piotr Mardziel, Fangjing Wu, Preetam Amancharla, and Anupam Datta · 2020
Later among the works it cites.
Time series data augmentation for deep learning: A survey
Qingsong Wen, Liang Sun, Fan Yang, Xiaomin Song, Jingkun Gao, Xue Wang, and Huan Xu · 2020
Later among the works it cites.
Synthetic data generation with probabilistic bayesian networks
Grigoriy Gogoshin, Sergio Branciamore, and Andrei S Rodin · 2020
Later among the works it cites.
Quant GANs: Deep Generation of Financial Time Series
Magnus Wiese, Robert Knobloch, Ralf Korn, and Peter Kretschmer · 2020
Later among the works it cites.
Conditional Sig-Wasserstein GANs for Time Series Generation
Hao Ni, Lukasz Szpruch, Magnus Wiese, Shujian Liao, and Baoren Xiao · 2020
Later among the works it cites.
Fidelity and privacy of synthetic medical data
Ofer Mendelevitch and Michael D Lesh · 2021
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Winning the nist contest: A scalable and general approach to differentially private synthetic data
Ryan McKenna, Gerome Miklau, and Daniel Sheldon · 2021
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Decaf: Generating fair synthetic data using causally-aware generative networks
Boris van Breugel, Trent Kyono, Jeroen Berrevoets, and Mihaela van der Schaar · 2021
Later among the works it cites.
Improving the accuracy of global forecasting models using time series data augmentation
Kasun Bandara, Hansika Hewamalage, Yuan-Hao Liu, Yanfei Kang, and Christoph Bergmeir · 2021
Later among the works it cites.
Bias mitigated learning from differentially private synthetic data: A cautionary tale
Sahra Ghalebikesabi, Harrison Wilde, Jack Jewson, Arnaud Doucet, Sebastian Vollmer, and Chris Holmes · 2021
Later among the works it cites.
Foundations of bayesian learning from synthetic data
Harrison Wilde, Jack Jewson, Sebastian Vollmer, and Chris Holmes · 2021
Later among the works it cites.
Representative & fair synthetic data
Paul Tiwald, Alexandra Ebert, and Daniel T Soukup · 2021
Later among the works it cites.
Membership inference attacks on machine learning: A survey
Hongsheng Hu, Zoran Salcic, Gillian Dobbie, and Xuyun Zhang · 2021
Later among the works it cites.
Statistical inference is not a privacy violation, 2021
Mark Bun, Damien Desfontaines, Cynthia Dwork, Moni Naor, Kobbi Nissim, Aaron Roth, Adam Smith, Thomas Steinke, Jonathan Ullman, and Salil Vadhan · 2021
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Antipodes of label differential privacy: Pate and alibi
Mani Malek Esmaeili, Ilya Mironov, Karthik Prasad, Igor Shilov, and Florian Tramer · 2021
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Ahmed M Alaa, Boris van Breugel, Evgeny Saveliev, and Mihaela van der Schaar · 2021
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Covariance’s loss is privacy’s gain: Computationally efficient, private and accurate synthetic data
March Boedihardjo, Thomas Strohmer, and Roman Vershynin · 2021
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Holdout-based empirical assessment of mixed-type synthetic data
Michael Platzer and Thomas Reutterer · 2021
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G-pate: Scalable differentially private data generator via private aggregation of teacher discriminators
Yunhui Long, Boxin Wang, Zhuolin Yang, Bhavya Kailkhura, Aston Zhang, Carl Gunter, and Bo Li · 2021
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Benchmarking differentially private synthetic data generation algorithms
Yuchao Tao, Ryan McKenna, Michael Hay, Ashwin Machanavajjhala, and Gerome Miklau · 2021
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Comparative study of differentially private synthetic data algorithms from the nist pscr differential privacy synthetic data challenge
Claire McKay Bowen and Joshua Snoke · 2021
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Measuring utility and privacy of synthetic genomic data
Bristena Oprisanu, Georgi Ganev, and Emiliano De Cristofaro · 2021
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Mayana Pereira, Meghana Kshirsagar, Sumit Mukherjee, Rahul Dodhia, and Juan Lavista Ferres · 2021
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Robin hood and matthew effects–differential privacy has disparate impact on synthetic data
Georgi Ganev, Bristena Oprisanu, and Emiliano De Cristofaro · 2021
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Can you fake it until you make it? impacts of differentially private synthetic data on downstream classification fairness
Victoria Cheng, Vinith M Suriyakumar, Natalie Dullerud, Shalmali Joshi, and Marzyeh Ghassemi · 2021
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Privsyn: Differentially private data synthesis
Zhikun Zhang, Tianhao Wang, Ninghui Li, Jean Honorio, Michael Backes, Shibo He, Jiming Chen, and Yang Zhang · 2021
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A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan · 2021
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