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Synthetic data is often presented as a method for sharing sensitive information in a privacy-preserving manner by reproducing the global statistical properties of the original data without disclosing sensitive information about any individual.
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Satisfying disclosure restrictions with synthetic data sets
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k-anonymity: A model for protecting privacy
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Jensen-Shannon divergence and Hilbert space embedding. In International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings. IEEE, 31
Bent Fuglede and Flemming Topsoe. 2004 · 2004
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Texas Hospital Discharge Data Public Use Data
Center for Health Statistics Texas Department of State Health Services. 2005 · 2005
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Differential Privacy
Cynthia Dwork. 2006 · 2006
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On the fundamentals of anonymity metrics. In IFIP International Summer School on the Future of Identity in the Information Society . Springer, 325–341
Christer Andersson and Reine Lundin. 2007 · 2007
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Internet traffic modeling by means of Hidden Markov Models
Alberto Dainotti, Antonio Pescapé, Pierluigi Salvo Rossi, Francesco Palmieri, and Giorgio Ventre. 2008 · 2008
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Robust de-anonymization of large sparse datasets. In 2008 IEEE Symposium on Security and Privacy (sp 2008) . IEEE, 111–125
Arvind Narayanan and Vitaly Shmatikov. 2008a · 2008
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Robust De-Anonymization of Large Sparse Datasets. In Proceedings of the 2008 IEEE Symposium on Security and Privacy (SP ’08) . IEEE Computer Society, USA, 111–125
Arvind Narayanan and Vitaly Shmatikov. 2008b · 2008
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On the Foundations of Quantitative Information Flow. 288–302
Geoffrey Smith. 2009 · 2009
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Using Bayesian networks to create synthetic data
Jim Young, Patrick Graham, and Richard Penny. 2009 · 2009
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Privacy-Preserving Data Publishing: A Survey of Recent Developments
Benjamin Fung, ke Wang, Rui Chen, and Philip Yu. 2010 · 2010
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Quantifying location privacy. In 2011 IEEE symposium on security and privacy . IEEE, 247–262
Reza Shokri, George Theodorakopoulos, Jean-Yves Le Boudec, and Jean-Pierre Hubaux. 2011 · 2011
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Synthetic Data – Anonymisation Groundhog Day
Theresa Stadler, Bristena Oprisanu, and Carmela Troncoso. 2020 · 2011
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Theoretical Results on De-Anonymization via Linkage Attacks
Martin M. Merener. 2012 · 2012
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The Algorithmic Foundations of Differential Privacy
Dwork, Cynthia and Roth, Aaron. 2013 · 2013
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Auto-Encoding Variational Bayes
Diederik P Kingma and Max Welling. 2013 · 2013
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Membership privacy: a unifying framework for privacy definitions. In Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security . 889–900
Ninghui Li, Wahbeh Qardaji, Dong Su, Yi Wu, and Weining Yang. 2013 · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
Cited alongside, same era.
Synthetic generation of high temporal resolution solar radiation data using Markov models
BO Ngoko, H Sugihara, and T Funaki. 2014 · 2014
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Opinion 05/2014 on anonymisation techniques
Article 29 Data Protection Working Party. 2014 · 2014
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Estimating the success of re-identifications in incomplete datasets using generative models
Luc Rocher, Julien Hendrickx, and Yves-Alexandre Montjoye. 2019 · 2019
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Modeling tabular data using conditional gan
Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni. 2019 · 2019
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Assessing privacy and quality of synthetic health data. In Proceedings of the Conference on Artificial Intelligence for Data Discovery and Reuse . 1–4
Andrew Yale, Saloni Dash, Ritik Dutta, Isabelle Guyon, Adrien Pavao, and Kristin P Bennett. 2019 · 2019
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Towards formalizing the GDPR’s notion of singling out
Aloni Cohen and Kobbi Nissim. 2020a · 2020
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Towards formalizing the GDPR’s notion of singling out
Aloni Cohen and Kobbi Nissim. 2020b · 2020
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Revisiting distance-based record linkage for privacy-preserving release of statistical datasets
Javier Herranz, Jordi Nin, Pablo Rodríguez, and Tamir Tassa. 2015 · 2015
Cited alongside, same era.
Deep Learning with Differential Privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016 · 2016
Cited alongside, same era.
Testing the robustness of anonymization techniques: acceptable versus unacceptable inferences. In The Brussels Privacy Symposium . brussels, Belgium
Gergely Acs, Claude Castelluccia, and Daniel Le Métayer. 2016 · 2016
Cited alongside, same era.
Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the Protection of Natural Persons with Regard to the Processing of Personal Data and on the Free Movement of Such Data, and Repealing Directive 95/46/EC (General Data Protection Regulation)
The European Union. 2016 · 2016
Cited alongside, same era.
Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals. 2016 · 2016
Cited alongside, same era.
Aircloak Challenge version 7
Aircloak. 2017 · 2017
Cited alongside, same era.
Wasserstein GAN
Martin Arjovsky, Soumith Chintala, and Léon Bottou. 2017 · 2017
Cited alongside, same era.
Evaluating Identity Disclosure Risk in Fully Synthetic Health Data: Model Development and Validation
Khaled El Emam, Lucy Mosquera, and Jason Bass. 2020 · 2020
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ML Privacy Meter: Aiding regulatory compliance by quantifying the privacy risks of machine learning
Sasi Kumar Murakonda and Reza Shokri. 2020 · 2020
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Code accompanying the ublication of Stadler et. al. 2020
Synhtetic Data Release. last accessed Sept. 2022 (latest commit is eba9a43) · 2020
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Side-channel attacks on query-based data anonymization. In Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security . 1254–1265
Franziska Boenisch, Reinhard Munz, Marcel Tiepelt, Simon Hanisch, Christiane Kuhn, and Paul Francis. 2021 · 2021
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Towards Improving Privacy of Synthetic DataSets. In Privacy Technologies and Policy , Nils Gruschka, Luís Filipe Coelho Antunes, Kai Rannenberg, and Prokopios Drogkaris (Eds.). Springer International Publishing, Cham, 106–119
Aditya Kuppa, Lamine Aouad, and Nhien-An Le-Khac. 2021 · 2021
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Towards Explaining Epsilon: A Worst-Case Study of Differential Privacy Risks. In 2021 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) . 328–331
Luise Mehner, Saskia Nuñez von Voigt, and Florian Tschorsch. 2021 · 2021
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Fidelity and Privacy of Synthetic Medical Data
Ofer Mendelevitch and Michael D. Lesh. 2021 · 2021
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Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning
Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, and Nicholas Carlini. 2021a · 2021
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Adversary instantiation: Lower bounds for differentially private machine learning. In 2021 IEEE Symposium on Security and Privacy (SP) . IEEE, 866–882
Milad Nasr, Shuang Songi, Abhradeep Thakurta, Nicolas Papemoti, and Nicholas Carlin. 2021b · 2021
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The Personal Information Protection and Electronic Documents Act (PIPEDA)
Office of the Privacy Commissioner of Canada. 2019 · 2021
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A Review of Anonymization for Healthcare Data
Iyiola E. Olatunji, Jens Rauch, Matthias Katzensteiner, and Megha Khosla. 2021 · 2021
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On Utility and Privacy in Synthetic Genomic Data
Bristena Oprisanu, Georgi Ganev, and Emiliano De Cristofaro. 2021 · 2021
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California Consumer Privacy Act (CCPA)
State of California Department of Justice. 2018 · 2021
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Attacks on Deidentification’s Defenses
Aloni Cohen. 2022 · 2022
Closest in time.
The market for synthetic data is bigger than you think
Tech Crunch. 2022 · 2022
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Gartner Unveils Top Predictions for IT Organizations and Users in 2022 and Beyond
Gartner. 2022a · 2022
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Synthetic Data Is About To Transform Artificial Intelligence
Rob Toews. 2022 · 2022
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By 2024, 60% of the data used for the development of AI and analytics projects will be synthetically generated
Andrew White. 2021 · 2024
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