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Generative models trained with Differential Privacy (DP) can produce synthetic data while reducing privacy risks.
Dynamic Programming
Richard Bellman. 1957 · 1957
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Calibrating noise to sensitivity in private data analysis. In TCC
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. 2006 · 2006
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On the complexity of differentially private data release: efficient algorithms and hardness results. In ACM STOC
Cynthia Dwork, Moni Naor, Omer Reingold, Guy N Rothblum, and Salil Vadhan. 2009 · 2009
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MNIST handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges. 2010 · 2010
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Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate. 2011 · 2011
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Synthesising the linked 2011 Census and deaths dataset while preserving its confidentiality
ONS DSC. 2023 · 2011
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PCPs and the hardness of generating private synthetic data. In TCC
Jonathan Ullman and Salil Vadhan. 2011 · 2011
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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Differentially private synthesization of multi-dimensional data using copula functions. In EDBT
Haoran Li, Li Xiong, and Xiaoqian Jiang. 2014 · 2014
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The composition theorem for differential privacy. In ICML
Peter Kairouz, Sewoong Oh, and Pramod Viswanath. 2015 · 2015
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Deep learning with differential privacy. In ACM CCS
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016 · 2016
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Practical differential privacy in high dimensions
Daniela S. Antonova. 2016 · 2016
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Principled evaluation of differentially private algorithms using dpbench. In SIGMOD
Michael Hay, Ashwin Machanavajjhala, Gerome Miklau, Yan Chen, and Dan Zhang. 2016 · 2016
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The Synthetic Data Vault
Neha Patki, Roy Wedge, and Kalyan Veeramachaneni. 2016 · 2016
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Wasserstein generative adversarial networks. In ICML
Martin Arjovsky, Soumith Chintala, and Léon Bottou. 2017 · 2017
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Real-valued (medical) time series generation with recurrent conditional gans
Cristóbal Esteban, Stephanie L Hyland, and Gunnar Rätsch. 2017 · 2017
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Semi-supervised knowledge transfer for deep learning from private training data. In ICLR
Nicolas Papernot, Martín Abadi, Ulfar Erlingsson, Ian Goodfellow, and Kunal Talwar. 2017 · 2017
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DataSynthesizer: Privacy-Preserving Synthetic Datasets. In SSDBM
Haoyue Ping, Julia Stoyanovich, and Bill Howe. 2017 · 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 · 2017
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Privacy preserving synthetic data release using deep learning. In ECML PKDD
Nazmiye Ceren Abay, Yan Zhou, Murat Kantarcioglu, Bhavani Thuraisingham, and Latanya Sweeney. 2018 · 2018
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Differentially private mixture of generative neural networks
Gergely Acs, Luca Melis, Claude Castelluccia, and Emiliano De Cristofaro. 2018 · 2018
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Privacy amplification by subsampling: Tight analyses via couplings and divergences
Borja Balle, Gilles Barthe, and Marco Gaboardi. 2018 · 2018
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Mutual information neural estimation. In ICML
Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, and Devon Hjelm. 2018 · 2018
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The creation and use of the SIPP synthetic Beta v7. 0
Gary Benedetto, Jordan C Stanley, Evan Totty, et al · 2018
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PATE-GAN: Generating synthetic data with differential privacy guarantees. In ICLR
James Jordon, Jinsung Yoon, and Mihaela Van Der Schaar. 2018 · 2018
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2018 Differential Privacy Synthetic Data Challenge
NIST. 2018a · 2018
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2018 The Unlinkable Data Challenge
NIST. 2018b · 2018
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Scalable private learning with pate. In ICLR
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson. 2018 · 2018
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Differentially private generative adversarial network
Liyang Xie, Kaixiang Lin, Shu Wang, Fei Wang, and Jiayu Zhou. 2018 · 2018
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Differentially private releasing via deep generative model (technical report)
Xinyang Zhang, Shouling Ji, and Ting Wang. 2018 · 2018
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Differential Privacy Synthetic Data Generation using WGANs
Moustafa Alzantot and Mani Srivastava. 2019 · 2019
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The secret sharer: Evaluating and testing unintended memorization in neural networks. In USENIX Security
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song. 2019 · 2019
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Ron-gauss: Enhancing utility in non-interactive private data release
Thee Chanyaswad, Changchang Liu, and Prateek Mittal. 2019 · 2019
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Differentially private generative adversarial networks for time series, continuous, and discrete open data. In IFIP SEC
Lorenzo Frigerio, Anderson Santana de Oliveira, Laurent Gomez, and Patrick Duverger. 2019 · 2019
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Logan: Membership inference attacks against generative models. In PoPETs
Jamie Hayes, Luca Melis, George Danezis, and Emiliano De Cristofaro. 2019 · 2019
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Monte Carlo and Reconstruction Membership Inference Attacks against Generative Models. In PoPETs
Benjamin Hilprecht, Martin Härterich, and Daniel Bernau. 2019 · 2019
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Evaluating differentially private machine learning in practice. In USENIX Security 19
Bargav Jayaraman and David Evans. 2019 · 2019
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Empirical Evaluation on Synthetic Data Generation with Generative Adversarial Network. In WIMS
Pei-Hsuan Lu, Pang-Chieh Wang, and Chia-Mu Yu. 2019 · 2019
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Graphical-model based estimation and inference for differential privacy. In ICML
Ryan McKenna, Daniel Sheldon, and Gerome Miklau. 2019 · 2019
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R \ \backslash ’enyi differential privacy of the sampled gaussian mechanism
Ilya Mironov, Kunal Talwar, and Li Zhang. 2019 · 2019
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Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data. In ICML
Georgi Ganev, Bristena Oprisanu, and Emiliano De Cristofaro. 2022 · 2022
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Mixed Differential Privacy in Computer Vision. In CVPR
Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Aaron Roth, Michael Kearns, and Stefano Soatto. 2022 · 2022
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Differentially Private Data Generation Needs Better Features
Fredrik Harder, Milad Jalali Asadabadi, Danica J Sutherland, and Mijung Park. 2022 · 2022
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TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Data. In NeurIPS SyntheticData4ML
Florimond Houssiau, James Jordon, Samuel N Cohen, Andrew Elliott, James Geddes, Callum Mole, Camila Rangel-Smith, and Lukasz Szpruch. 2022 · 2022
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Chapter 5: Privacy-enhancing technologies (PETs)
ICO UK. 2022 · 2022
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On variational bounds of mutual information. In ICML
Ben Poole, Sherjil Ozair, Aaron Van Den Oord, Alex Alemi, and George Tucker. 2019 · 2019
Cited alongside, same era.
Detecting overfitting of deep generative networks via latent recovery. In IEEE CVPR
Ryan Webster, Julien Rabin, Loic Simon, and Frédéric Jurie. 2019 · 2019
Cited alongside, same era.
Differentially private high-dimensional data publication via Markov network
Wei Zhang, Jingwen Zhao, Fengqiong Wei, and Yunfang Chen. 2019 · 2019
Cited alongside, same era.
Christian Arnold and Marcel Neunhoeffer. 2020 · 2020
Cited alongside, same era.
Gan-leaks: A taxonomy of membership inference attacks against generative models. In ACM CCS
Dingfan Chen, Ning Yu, Yang Zhang, and Mario Fritz. 2020 · 2020
Cited alongside, same era.
2020 Differential Privacy Temporal Map Challenge
NIST. 2020 · 2020
Cited alongside, same era.
Telescoping density-ratio estimation
Benjamin Rhodes, Kai Xu, and Michael U Gutmann. 2020 · 2020
Cited alongside, same era.
Synthetic Data–what, why and how?
James Jordon, Lukasz Szpruch, Florimond Houssiau, Mirko Bottarelli, Giovanni Cherubin, Carsten Maple, Samuel N Cohen, and Adrian Weller. 2022 · 2022
Later among the works it cites.
Optimizing Random Mixup with Gaussian Differential Privacy
Donghao Li, Yang Cao, and Yuan Yao. 2022a · 2022
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Large language models can be strong differentially private learners
Xuechen Li, Florian Tramer, Percy Liang, and Tatsunori Hashimoto. 2022b · 2022
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Yucong Liu, Chi-Hua Wang, and Guang Cheng. 2022a · 2022
Later among the works it cites.
dpart: Differentially Private Autoregressive Tabular, a General Framework for Synthetic Data Generation
Sofiane Mahiou, Kai Xu, and Georgi Ganev. 2022 · 2022
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A simple recipe for private synthetic data generation
Ryan McKenna and Terrance Liu. 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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IOM and Microsoft release first-ever differentially private synthetic dataset to counter human trafficking
Microsoft. 2022 · 2022
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Can a Model Be Differentially Private and Fair?
Adam Pearce. 2022 · 2022
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Spending Privacy Budget Fairly and Wisely
Lucas Rosenblatt, Joshua Allen, and Julia Stoyanovich. 2022 · 2022
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Synthetic Data – Anonymization Groundhog Day. In Usenix Security
Theresa Stadler, Bristena Oprisanu, and Carmela Troncoso. 2022 · 2022
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Benchmarking differentially private synthetic data generation algorithms
Yuchao Tao, Ryan McKenna, Michael Hay, Ashwin Machanavajjhala, and Gerome Miklau. 2022 · 2022
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The market for synthetic data is bigger than you think
TechCrunch. 2022 · 2022
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Private synthetic data for multitask learning and marginal queries
Giuseppe Vietri, Cedric Archambeau, Sergul Aydore, William Brown, Michael Kearns, Aaron Roth, Ankit Siva, Shuai Tang, and Steven Z Wu. 2022 · 2022
Later among the works it cites.
Differentially private fine-tuning of language models
Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, et al · 2022
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Realistic test data in minutes
Accelario. 2023 · 2023
Closest in time.
Guide: Synthetic Data
Datagen. 2023 · 2023
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Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano. In ICML
Chuan Guo, Alexandre Sablayrolles, and Maziar Sanjabi. 2023 · 2023
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Bounding Training Data Reconstruction in DP-SGD
Jamie Hayes, Saeed Mahloujifar, and Borja Balle. 2023 · 2023
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Model Parameters
Hazy. 2023 · 2023
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Synthetic data to test the effectiveness of a vulnerable person’s detection system in financial services
ICO UK. 2023 · 2023
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Statistical Theory of Differentially Private Marginal-based Data Synthesis Algorithms. In ICLR
Ximing Li, Chendi Wang, and Guang Cheng. 2023 · 2023
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Group and Attack: Auditing Differential Privacy. In CCS
Johan Lokna, Anouk Paradis, Dimitar I Dimitrov, and Martin Vechev. 2023 · 2023
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Tight Auditing of Differentially Private Machine Learning. In USENIX Security
Milad Nasr, Jamie Hayes, Thomas Steinke, Borja Balle, Florian Tramèr, Matthew Jagielski, Nicholas Carlini, and Andreas Terzis. 2023 · 2023
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Synthcity: facilitating innovative use cases of synthetic data in different data modalities
Zhaozhi Qian, Bogdan-Constantin Cebere, and Mihaela van der Schaar. 2023 · 2023
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AI-generated Synthetic Data, easy and fast access to high quality data?
Syntho. 2023 · 2023
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Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD
Anvith Thudi, Hengrui Jia, Casey Meehan, Ilia Shumailov, and Nicolas Papernot. 2023 · 2023
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DPMLBench: Holistic Evaluation of Differentially Private Machine Learning. In ACM CCS
Chengkun Wei, Minghu Zhao, Zhikun Zhang, Min Chen, Wenlong Meng, Bo Liu, Yuan Fan, and Wenzhi Chen. 2023 · 2023
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Using Synthetic Data in Financial Services
FCA UK. 2024 · 2024
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Differentially Private Release of Israel’s National Registry of Live Births
Shlomi Hod and Ran Canetti. 2024 · 2024
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South Australian Health Partners with Gretel to Pioneer State-Wide Synthetic Data Initiative for Safe EHR Data Sharing
Microsoft. 2024 · 2024
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