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We propose AIM, a new algorithm for differentially private synthetic data generation.
Differentially Private Release of High-Dimensional Datasets using the Gaussian Copula
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Differentially private data cubes: optimizing noise sources and consistency. In Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2011, Athens, Greece, June 12-16, 2011 , Timos K. Sellis, Renée J. Miller, Anastasios Kementsietsidis, and Yannis Velegrakis (Eds.). ACM, 217–228
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A Simple and Practical Algorithm for Differentially Private Data Release. In Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012, Lake Tahoe, Nevada, United States , Peter L. Bartlett, Fernando C. N. Pereira, Christopher J. C. Burges, Léon Bottou, and Kilian Q. Weinberger (Eds.). 2348–2356
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The geometry of differential privacy: the sparse and approximate cases. In Proceedings of the forty-fifth annual ACM symposium on Theory of computing . 351–360
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Generative Adversarial Nets. In Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada , Zoubin Ghahramani, Max Welling, Corinna Cortes, Neil D. Lawrence, and Kilian Q. Weinberger (Eds.). 2672–2680
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Differentially Private Synthesization of Multi-Dimensional Data using Copula Functions. In Proceedings of the 17th International Conference on Extending Database Technology, EDBT 2014, Athens, Greece, March 24-28, 2014 , Sihem Amer-Yahia, Vassilis Christophides, Anastasios Kementsietsidis, Minos N. Garofalakis, Stratos Idreos, and Vincent Leroy (Eds.). OpenProceedings.org, 475–486
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On the folded normal distribution
Michail Tsagris, Christina Beneki, and Hossein Hassani. 2014 · 2014
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Differentially private high-dimensional data publication via sampling-based inference. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 129–138
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Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds. In Theory of Cryptography Conference . Springer, 635–658
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Differentially Private High-Dimensional Data Publication via Markov Network
Wei Zhang, Jingwen Zhao, Fengqiong Wei, and Yunfang Chen. 2019 · 2019
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The Discrete Gaussian for Differential Privacy. In NeurIPS
Clément L. Canonne, Gautam Kamath, and Thomas Steinke. 2020 · 2020
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New Oracle-Efficient Algorithms for Private Synthetic Data Release. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event (Proceedings of Machine Learning Research) , Vol. 119. PMLR, 9765–9774
Giuseppe Vietri, Grace Tian, Mark Bun, Thomas Steinke, and Zhiwei Steven Wu. 2020 · 2020
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Differentially Private Query Release Through Adaptive Projection. In Proceedings of the 38th International Conference on Machine Learning (Proceedings of Machine Learning Research) , Marina Meila and Tong Zhang (Eds.), Vol. 139. PMLR, 457–467
Sergul Aydore, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, and Ankit A Siva. 2021 · 2021
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Principled evaluation of differentially private algorithms using dpbench. In Proceedings of the 2016 International Conference on Management of Data . 139–154
Michael Hay, Ashwin Machanavajjhala, Gerome Miklau, Yan Chen, and Dan Zhang. 2016 · 2016
Cited alongside, same era.
Model-based differentially private data synthesis
Fang Liu. 2016 · 2016
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Plausible Deniability for Privacy-Preserving Data Synthesis
Vincent Bindschaedler, Reza Shokri, and Carl A. Gunter. 2017 · 2017
Cited alongside, same era.
DPPro: Differentially Private High-Dimensional Data Release via Random Projection
Chugui Xu, Ju Ren, Yaoxue Zhang, Zhan Qin, and Kui Ren. 2017 · 2017
Cited alongside, same era.
PrivBayes: Private Data Release via Bayesian Networks
Jun Zhang, Graham Cormode, Cecilia M. Procopiuc, Divesh Srivastava, and Xiaokui Xiao. 2017 · 2017
Cited alongside, same era.
Privacy Preserving Synthetic Data Release Using Deep Learning. In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part I (Lecture Notes in Computer Science) , Michele Berlingerio, Francesco Bonchi, Thomas Gärtner, Neil Hurley, and Georgiana Ifrim (Eds.), Vol. 11051. Springer, 510–526
Nazmiye Ceren Abay, Yan Zhou, Murat Kantarcioglu, Bhavani M. Thuraisingham, and Latanya Sweeney. 2018 · 2018
Cited alongside, same era.
Optimizing error of high-dimensional statistical queries under differential privacy
Ryan McKenna, Gerome Miklau, Michael Hay, and Ashwin Machanavajjhala. 2018 · 2018
Cited alongside, same era.
Differentially Private Generative Adversarial Network
Liyang Xie, Kaixiang Lin, Shu Wang, Fei Wang, and Jiayu Zhou. 2018 · 2018
Cited alongside, same era.
Data synthesis via differentially private markov random fields
Kuntai Cai, Xiaoyu Lei, Jianxin Wei, and Xiaokui Xiao. 2021 · 2021
Later among the works it cites.
Bounding, Concentrating, and Truncating: Unifying Privacy Loss Composition for Data Analytics. In Proceedings of the 32nd International Conference on Algorithmic Learning Theory (Proceedings of Machine Learning Research) , Vitaly Feldman, Katrina Ligett, and Sivan Sabato (Eds.), Vol. 132. PMLR, 421–457
Mark Cesar and Ryan Rogers. 2021 · 2021
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Individual privacy accounting via a renyi filter
Vitaly Feldman and Tijana Zrnic. 2021 · 2021
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Kamino: Constraint-Aware Differentially Private Data Synthesis
Chang Ge, Shubhankar Mohapatra, Xi He, and Ihab F. Ilyas. 2021 · 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. 2021a · 2021
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Relaxed Marginal Consistency for Differentially Private Query Answering
Ryan McKenna, Siddhant Pradhan, Daniel R Sheldon, and Gerome Miklau. 2021b · 2021
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Hyperparameter Tuning with Renyi Differential Privacy
Nicolas Papernot and Thomas Steinke. 2021 · 2021
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Challenge Design and Lessons Learned from the 2018 Differential Privacy Challenges
Diane Ridgeway, Mary Theofanos, Terese Manley, Christine Task, et al · 2021
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Benchmarking Differentially Private Synthetic Data Generation Algorithms
Yuchao Tao, Ryan McKenna, Michael Hay, Ashwin Machanavajjhala, and Gerome Miklau. 2021 · 2021
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PrivSyn: Differentially Private Data Synthesis. In 30th USENIX Security Symposium (USENIX Security 21) . USENIX Association, 929–946
Zhikun Zhang, Tianhao Wang, Ninghui Li, Jean Honorio, Michael Backes, Shibo He, Jiming Chen, and Yang Zhang. 2021 · 2021
Later among the works it cites.
A simple recipe for private synthetic data generation
Ryan McKenna and Terrance Liu. 2022 · 2022
Closest in time.
AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data
Ryan McKenna, Brett Mullins, Daniel Sheldon, and Gerome Miklau. 2022 · 2022
Closest in time.
Differentially private synthetic medical data generation using convolutional gans
Amirsina Torfi, Edward A Fox, and Chandan K Reddy. 2022 · 2022
Closest in time.