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Generative models trained with Differential Privacy (DP) can be used to generate synthetic data while minimizing privacy risks.
Mechanism design via differential privacy
McSherry, F. and Talwar, K · 2007
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Probabilistic graphical models: principles and techniques
Koller, D. and Friedman, N · 2009
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MNIST handwritten digit database
LeCun, Y., Cortes, C., and Burges, C · 2010
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Differentially private empirical risk minimization
Chaudhuri, K., Monteleoni, C., and Sarwate, A. D · 2011
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Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E · 2011
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Bayesian reasoning and machine learning
Barber, D · 2012
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Texas Hospital Inpatient Discharge Public Use Data File Q1-Q4, 2013
DSHS · 2013
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Acquire Valued Shoppers Challenge
Kaggle · 2013
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The algorithmic foundations of differential privacy
Dwork, C., Roth, A., et al · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Deep learning with differential privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., and Zhang, L · 2016
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Equality of opportunity in supervised learning
Hardt, M., Price, E., and Srebro, N · 2016
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Semi-supervised knowledge transfer for deep learning from private training data
Papernot, N., Abadi, M., Erlingsson, U., Goodfellow, I., and Talwar, K · 2016
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Wasserstein generative adversarial networks
Arjovsky, M., Chintala, S., and Bottou, L · 2017
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UCI Machine Learning Repository
Dua, D. and Graff, C · 2017
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DataSynthesizer
Ping, H., Stoyanovich, J., and Howe, B · 2017
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Membership inference attacks against machine learning models
Shokri, R., Stronati, M., Song, C., and Shmatikov, V · 2017
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Privbayes: Private data release via bayesian networks
Zhang, J., Cormode, G., Procopiuc, C. M., Srivastava, D., and Xiao, X · 2017
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Differentially private mixture of generative neural networks
Acs, G., Melis, L., Castelluccia, C., and De Cristofaro, E · 2018
Cited alongside, same era.
The creation and use of the SIPP synthetic Beta v7. 0
Benedetto, G., Stanley, J. C., Totty, E., et al · 2018
Cited alongside, same era.
PATE-GAN: Generating synthetic data with differential privacy guarantees
Jordon, J., Yoon, J., and Van Der Schaar, M · 2018
Cited alongside, same era.
2018 Differential Privacy Synthetic Data Challenge
NIST · 2018
Cited alongside, same era.
2018 The Unlinkable Data Challenge
NIST · 2018
Cited alongside, same era.
Scalable private learning with pate
Papernot, N., Song, S., Mironov, I., Raghunathan, A., Talwar, K., and Erlingsson, Ú · 2018
Neither private nor fair: Impact of data imbalance on utility and fairness in differential privacy
Farrand, T., Mireshghallah, F., Singh, S., and Trask, A · 2020
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Does learning require memorization? a short tale about a long tail
Feldman, V · 2020
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Census 2020 +/- 2: Census, Differential Privacy, and the Future of Data
Hong, J · 2020
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Synthetic data: Breaking the data logjam in machine learning for healthcare
Van Der Schaar, M. and Maxfield, N · 2020
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Changes to the Census Could Make Small Towns Disappear
Wezerek, G. and Van Riper, D · 2020
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Can You Fake It Until You Make It? Impacts of Differentially Private Synthetic Data on Downstream Classification Fairness
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Cited alongside, same era.
Differentially private generative adversarial network
Xie, L., Lin, K., Wang, S., Wang, F., and Zhou, J · 2018
Cited alongside, same era.
Differential Privacy Synthetic Data Generation using WGANs
Alzantot, M. and Srivastava, M · 2019
Cited alongside, same era.
Differential privacy has disparate impact on model accuracy
Bagdasaryan, E., Poursaeed, O., and Shmatikov, V · 2019
Cited alongside, same era.
The secret sharer: Evaluating and testing unintended memorization in neural networks
Carlini, N., Liu, C., Erlingsson, Ú., Kos, J., and Song, D · 2019
Cited alongside, same era.
Differentially private generative adversarial networks for time series, continuous, and discrete open data
Frigerio, L., de Oliveira, A. S., Gomez, L., and Duverger, P · 2019
Cited alongside, same era.
Logan: Membership inference attacks against generative models
Hayes, J., Melis, L., Danezis, G., and De Cristofaro, E · 2019
Cited alongside, same era.
Cheng, V., Suriyakumar, V. M., Dullerud, N., Joshi, S., and Ghassemi, M · 2021
Closest in time.
Bias Mitigated Learning from Differentially Private Synthetic Data: A Cautionary Tale
Ghalebikesabi, S., Wilde, H., Jewson, J., Doucet, A., Vollmer, S., and Holmes, C · 2021
Closest in time.
ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models
Liu, Y., Wen, R., He, X., Salem, A., Zhang, Z., Backes, M., De Cristofaro, E., Fritz, M., and Zhang, Y · 2021
Closest in time.
Winning the NIST Contest: A scalable and general approach to differentially private synthetic data
McKenna, R., Miklau, G., and Sheldon, D · 2021
Closest in time.
A&E Synthetic Data
NHS England · 2021
Closest in time.
Pereira, M., Kshirsagar, M., Mukherjee, S., Dodhia, R., and Ferres, J. L · 2021
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Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings
Suriyakumar, V. M., Papernot, N., Goldenberg, A., and Ghassemi, M · 2021
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Differentially private mixed-type data generation for unsupervised learning
Tantipongpipat, U., Waites, C., Boob, D., Siva, A., and Cummings, R · 2021
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DP-SGD vs PATE: Which Has Less Disparate Impact on Model Accuracy?
Uniyal, A., Naidu, R., Kotti, S., Singh, S., Kenfack, P. J., Mireshghallah, F., and Trask, A · 2021
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Differential Privacy and the 2020 Census
US Census Bureau · 2021
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Privsyn: Differentially private data synthesis
Zhang, Z., Wang, T., Li, N., Honorio, J., Backes, M., He, S., Chen, J., and Zhang, Y · 2021
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Synthetic Data – Anonymization Groundhog Day
Stadler, T., Oprisanu, B., and Troncoso, C · 2022
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