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Data synthesis has been advocated as an important approach for utilizing data while protecting data privacy.
Membership inference attacks from first principles. In 2022 IEEE Symposium on Security and Privacy (SP) . 1897–1914
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, and Florian Tramer. 2022 · 1914
Earlier work this paper cites.
A review of methods for missing data
Therese D Pigott. 2001 · 2001
Earlier work this paper cites.
k-anonymity: A model for protecting privacy
Latanya Sweeney. 2002 · 2002
Earlier work this paper cites.
Differential Privacy. In Automata, Languages and Programming . 1–12
Cynthia Dwork. 2006 · 2006
Earlier work this paper cites.
l-Diversity: Privacy Beyond k-Anonymity. In Proceedings of the 22nd International Conference on Data Engineering . 24
Ashwin Machanavajjhala, Johannes Gehrke, Daniel Kifer, and Muthuramakrishnan Venkitasubramaniam. 2006 · 2006
Earlier work this paper cites.
t-closeness: Privacy beyond k-anonymity and l-diversity. In 2007 IEEE 23rd international conference on data engineering . 106–115
Ninghui Li, Tiancheng Li, and Suresh Venkatasubramanian. 2007 · 2007
Earlier work this paper cites.
Visualizing data using t-SNE
Laurens Van der Maaten and Geoffrey Hinton. 2008 · 2008
Earlier work this paper cites.
Sinkhorn distances: Lightspeed computation of optimal transport. In Proceedings of the 26 th International Conference on Neural Information Processing Systems
Marco Cuturi. 2013 · 2013
Earlier work this paper cites.
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
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth. 2014 · 2014
Earlier work this paper cites.
Sliced and radon wasserstein barycenters of measures
Nicolas Bonneel, Julien Rabin, Gabriel Peyré, and Hanspeter Pfister. 2015 · 2015
Earlier work this paper cites.
Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining . 785–794
Tianqi Chen and Carlos Guestrin. 2016 · 2016
Earlier work this paper cites.
CVXPY: A Python-embedded modeling language for convex optimization
Steven Diamond and Stephen Boyd. 2016 · 2016
Earlier work this paper cites.
Differential privacy: From theory to practice
Ninghui Li, Min Lyu, Dong Su, and Weining Yang. 2016 · 2016
Earlier work this paper cites.
The Synthetic data vault. In IEEE International Conference on Data Science and Advanced Analytics . 399–410
Neha Patki, Roy Wedge, and Kalyan Veeramachaneni. 2016 · 2016
Earlier work this paper cites.
A note on the evaluation of generative models. In ICLR
L. Theis, A. van den Oord, and M. Bethge. 2016 · 2016
Earlier work this paper cites.
Improved training of wasserstein gans. In Proceedings of the 31th International Conference on Neural Information Processing Systems
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville. 2017 · 2017
Earlier work this paper cites.
Membership inference attacks against machine learning models. In 2017 IEEE symposium on security and privacy (SP) . 3–18
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2017 · 2017
Earlier work this paper cites.
Attention is all you need. In Proceedings of the 31th International Conference on Neural Information Processing Systems
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Privbayes: Private data release via bayesian networks
Jun Zhang, Graham Cormode, Cecilia M Procopiuc, Divesh Srivastava, and Xiaokui Xiao. 2017 · 2017
Earlier work this paper cites.
The creation and use of the SIPP Synthetic
Gary Benedetto, Martha Stinson, and John M Abowd. 2018 · 2018
Earlier work this paper cites.
PATE-GAN: Generating synthetic data with differential privacy guarantees. In International conference on learning representations
James Jordon, Jinsung Yoon, and Mihaela Van Der Schaar. 2018 · 2018
Earlier work this paper cites.
The ATEN Framework for Creating the Realistic Synthetic Electronic Health Record. In International Conference on Health Informatics
Scott McLachlan, Kudakwashe Dube, Thomas Gallagher, Bridget J. Daley, and Jason A. Walonoski. 2018 · 2018
Earlier work this paper cites.
Differential privacy synthetic data challenge
NIST. 2018 · 2018
Earlier work this paper cites.
Scalable Private Learning with PATE. In International Conference on Learning Representations
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Ulfar Erlingsson. 2018 · 2018
Earlier work this paper cites.
Data Synthesis based on Generative Adversarial Networks
Noseong Park, Mahmoud Mohammadi, Kshitij Gorde, Sushil Jajodia, Hongkyu Park, and Youngmin Kim. 2018 · 2018
Earlier work this paper cites.
CatBoost: unbiased boosting with categorical features. In Proceedings of the 32nd International Conference on Neural Information Processing Systems
Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, and Andrey Gulin. 2018 · 2018
Earlier work this paper cites.
General and specific utility measures for synthetic data
Joshua Snoke, Gillian M Raab, Beata Nowok, Chris Dibben, and Aleksandra Slavkovic. 2018 · 2018
Cited alongside, same era.
Claire McKay Bowen and Joshua Snoke. 2019 · 2019
Cited alongside, same era.
Graphical-model based estimation and inference for differential privacy. In International Conference on Machine Learning . 4435–4444
Ryan McKenna, Daniel Sheldon, and Gerome Miklau. 2019 · 2019
Cited alongside, same era.
Computational optimal transport: With applications to data science
Gabriel Peyré and Marco Cuturi. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Cited alongside, same era.
STaSy: Score-based Tabular data Synthesis. In International Conference on Learning Representations
Jayoung Kim, Chaejeong Lee, and Noseong Park. 2022 · 2022
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Score-based Generative Modeling Secretly Minimizes the Wasserstein Distance. In Proceedings of the 36th International Conference on Neural Information Processing Systems
Dohyun Kwon, Ying Fan, and Kangwook Lee. 2022 · 2022
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AIM: an adaptive and iterative mechanism for differentially private synthetic data
Ryan McKenna, Brett Mullins, Daniel Sheldon, and Gerome Miklau. 2022 · 2022
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On Utility and Privacy in Synthetic Genomic Data. In 29th Annual Network and Distributed System Security Symposium
Bristena Oprisanu, Georgi Ganev, and Emiliano De Cristofaro. 2022 · 2022
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Synthetic data – anonymisation groundhog day. In 31st USENIX Security Symposium . 1451–1468
Theresa Stadler, Bristena Oprisanu, and Carmela Troncoso. 2022 · 2022
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Modeling tabular data using conditional GAN. In Proceedings of the 33rd International Conference on Neural Information Processing Systems . 7335–7345
Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni. 2019 · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Christian Arnold and Marcel Neunhoeffer. 2020 · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models. In Proceedings of the 34th International Conference on Neural Information Processing Systems . 6840–6851
Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020 · 2020
Cited alongside, same era.
Differential privacy temporal map challenge
NIST. 2020 · 2020
Cited alongside, same era.
Synthesising Tabular Data using Wasserstein Conditional GANs with Gradient Penalty (WCGAN-GP). In Irish Conference on Artificial Intelligence and Cognitive Science
Manhar Walia, Brendan Tierney, and Susan Mckeever. 2020 · 2020
Cited alongside, same era.
Pot: Python optimal transport
Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z. Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T. H. Gayraud, Hicham Janati, Alain Rakotomamonjy, Ievgen Redko, Antoine Rolet, Antony Schutz, Vivien Seguy, Danica J. Sutherland, Romain Tavenard, Alexander Tong, and Titouan Vayer. 2021 · 2021
Cited alongside, same era.
Later among the works it cites.
Language Models are Realistic Tabular Data Generators. In International Conference on Learning Representations
Vadim Borisov, Kathrin Sessler, Tobias Leemann, Martin Pawelczyk, and Gjergji Kasneci. 2023 · 2023
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Extracting training data from diffusion models. In 32nd USENIX Security Symposium . 5253–5270
Nicolas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramer, Borja Balle, Daphne Ippolito, and Eric Wallace. 2023 · 2023
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Are diffusion models vulnerable to membership inference attacks?. In International Conference on Machine Learning . 8717–8730
Jinhao Duan, Fei Kong, Shiqi Wang, Xiaoshuang Shi, and Kaidi Xu. 2023 · 2023
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On the quality of synthetic generated tabular data
Erica Espinosa and Alvaro Figueira. 2023 · 2023
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Build smarter with the right data. fast. safe. accurate
Gretel. 2023 · 2023
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Tabddpm: Modelling tabular data with diffusion models. In International Conference on Machine Learning . 17564–17579
Akim Kotelnikov, Dmitry Baranchuk, Ivan Rubachev, and Artem Babenko. 2023 · 2023
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Achilles’ heels: vulnerable record identification in synthetic data publishing. In European Symposium on Research in Computer Security . 380–399
Matthieu Meeus, Florent Guepin, Ana-Maria Creţu, and Yves-Alexandre de Montjoye. 2023 · 2023
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Emerging privacy-enhancing technologies
OECD. 2023 · 2023
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REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers
Aivin V. Solatorio and Olivier Dupriez. 2023 · 2023
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Generating tabular datasets under differential privacy
Gianluca Truda. 2023 · 2023
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Membership Inference Attacks against Synthetic Data through Overfitting Detection. In International Conference on Artificial Intelligence and Statistics , Francisco J. R. Ruiz, Jennifer G. Dy, and Jan-Willem van de Meent (Eds.). 3493–3514
Boris van Breugel, Hao Sun, Zhaozhi Qian, and Mihaela van der Schaar. 2023 · 2023
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A Linear Reconstruction Approach for Attribute Inference Attacks against Synthetic Data. In 33rd USENIX Security Symposium . 2351–2368
Meenatchi Sundaram Muthu Selva Annamalai, Andrea Gadotti, and Luc Rocher. 2024 · 2024
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Do membership inference attacks work on large language models?
Michael Duan, Anshuman Suri, Niloofar Mireshghallah, Sewon Min, Weijia Shi, Luke Zettlemoyer, Yulia Tsvetkov, Yejin Choi, David Evans, and Hannaneh Hajishirzi. 2024 · 2024
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SoK: Privacy-Preserving Data Synthesis. In 2024 IEEE Symposium on Security and Privacy (SP) . 2–2
Yuzheng Hu, Fan Wu, Qinbin Li, Yunhui Long, Gonzalo Garrido, Chang Ge, Bolin Ding, David Forsyth, Bo Li, and Dawn Song. 2024 · 2024
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SynthEval: A Framework for Detailed Utility and Privacy Evaluation of Tabular Synthetic Data
Anton Danholt Lautrup, Tobias Hyrup, Arthur Zimek, and Peter Schneider-Kamp. 2024 · 2024
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An evaluation framework for synthetic data generation models. In IFIP International Conference on Artificial Intelligence Applications and Innovations . 320–335
Ioannis E Livieris, Nikos Alimpertis, George Domalis, and Dimitris Tsakalidis. 2024 · 2024
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Synthcity: a benchmark framework for diverse use cases of tabular synthetic data
Zhaozhi Qian, Rob Davis, and Mihaela van der Schaar. 2024 · 2024
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Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Space. In International Conference on Learning Representations
Hengrui Zhang, Jiani Zhang, Balasubramaniam Srinivasan, Zhengyuan Shen, Xiao Qin, Christos Faloutsos, Huzefa Rangwala, and George Karypis. 2024 · 2024
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Ctab-gan+: Enhancing tabular data synthesis
Zilong Zhao, Aditya Kunar, Robert Birke, Hiek Van der Scheer, and Lydia Y Chen. 2024 · 2024
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DP-2Stage: Adapting Language Models as Differentially Private Tabular Data Generators
Tejumade Afonja, Hui-Po Wang, Raouf Kerkouche, and Mario Fritz. 2025 · 2025
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The Inadequacy of Similarity-Based Privacy Metrics: Privacy Attacks Against “Truly Anonymous” Synthetic Datasets. In 2025 IEEE Symposium on Security and Privacy (SP) . 4007–4025
Georgi Ganev and Emiliano De Cristofaro. 2025 · 2025
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