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Personal data collected at scale promises to improve decision-making and accelerate innovation.
Revealing information while preserving privacy
Irit Dinur and Kobbi Nissim · 2003
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Differential privacy
Cynthia Dwork · 2006
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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
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Membership privacy: A unifying framework for privacy definitions
Ninghui Li, Wahbeh Qardaji, Dong Su, Yi Wu, and Weining Yang · 2013
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Robust traceability from trace amounts
Cynthia Dwork, Adam Smith, Thomas Steinke, Jonathan Ullman, and Salil Vadhan · 2015
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The composition theorem for differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2015
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Final report on the disclosure risk associated with the synthetic data produced by the sylls team
Mark Elliot · 2015
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Knock knock, who’s there? membership inference on aggregate location data
Apostolos Pyrgelis, Carmela Troncoso, and Emiliano De Cristofaro · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 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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Staring-down the database reconstruction theorem
John M Abowd · 2018
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Empirical evaluation on synthetic data generation with generative adversarial network
Pei-Hsuan Lu, Pang-Chieh Wang, and Chia-Mu Yu · 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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Logan: Membership inference attacks against generative models
Jamie Hayes, Luca Melis, George Danezis, and Emiliano De Cristofaro · 2019
Modeling tabular data using conditional gan
Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni · 2019
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Synthetic data–anonymisation groundhog day
Theresa Stadler, Bristena Oprisanu, and Carmela Troncoso · 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
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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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Adversary instantiation: Lower bounds for differentially private machine learning
Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, and Nicholas Carlini · 2021
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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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Membership inference attacks against synthetic health data
Ziqi Zhang, Chao Yan, and Bradley A Malin · 2022
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Bayesian estimation of differential privacy
Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Ahmed Salem, Victor Rühle, Andrew Paverd, Mohammad Naseri, and Boris Köpf · 2022
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