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Diferentially private (DP) synthetic datasets are a powerful approach for training machine learning models while respecting the privacy of individual data providers.
Privacy integrated queries: an extensible platform for privacy-preserving data analysis
McSherry, F. D · 2009
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Data preprocessing techniques for classification without discrimination
Kamiran, F. and Calders, T · 2012
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The algorithmic foundations of differential privacy
Dwork, C., Roth, A., et al · 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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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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Fairness in machine learning
Barocas, S., Hardt, M., and Narayanan, A · 2017
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Optimized pre-processing for discrimination prevention
Calmon, F. P., Wei, D., Vinzamuri, B., Ramamurthy, K. N., and Varshney, K. R · 2017
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UCI machine learning repository, 2017
Dua, D. and Graff, C · 2017
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Privacy loss in apple’s implementation of differential privacy on macos 10.12
Tang, J., Korolova, A., Bai, X., Wang, X., and Wang, X · 2017
Cited alongside, same era.
The us census bureau adopts differential privacy
Abowd, J. M · 2018
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Pate-gan: Generating synthetic data with differential privacy guarantees
Jordon, J., Yoon, J., and Van Der Schaar, M · 2018
Cited alongside, same era.
Differential privacy has disparate impact on model accuracy
Bagdasaryan, E., Poursaeed, O., and Shmatikov, V · 2019
Cited alongside, same era.
Propublica’s compas data revisited
Barenstein, M · 2019
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Google covid-19 community mobility reports: Anonymization process description (version 1.0)
Aktay, A., Bavadekar, S., Cossoul, G., Davis, J., Desfontaines, D., Fabrikant, A., Gabrilovich, E., Gadepalli, K., Gipson, B., Guevara, M., et al · 2020
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Predicting covid-19 pneumonia severity on chest x-ray with deep learning
Cohen, J. P., Dao, L., Roth, K., Morrison, P., Bengio, Y., Abbasi, A. F., Shen, B., Mahsa, H. K., Ghassemi, M., Li, H., et al · 2020
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Perrone, V., Donini, M., Zafar, M. B., Schmucker, R., Kenthapadi, K., and Archambeau, C · 2020
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Differentially private synthetic data: Applied evaluations and enhancements
Rosenblatt, L., Liu, X., Pouyanfar, S., de Leon, E., Desai, A., and Allen, J · 2020
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Heidari, H., Loi, M., Gummadi, K. P., and Krause, A · 2019
Cited alongside, same era.
Do no harm: a roadmap for responsible machine learning for health care
Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu, V. X., Doshi-Velez, F., Jung, K., Heller, K., Kale, D., Saeed, M., et al · 2019
Cited alongside, same era.
Modeling tabular data using conditional gan
Xu, L., Skoularidou, M., Cuesta-Infante, A., and Veeramachaneni, K · 2019
Cited alongside, same era.
Calibrating noise to sensitivity in private data analysis
Dwork, C., McSherry, F., Nissim, K., and Smith, A
Cited in the paper.
Calibrating noise to sensitivity in private data analysis
Dwork, C., McSherry, F., Nissim, K., and Smith, A
Cited in the paper.
Privbayes: Private data release via bayesian networks
Zhang, J., Cormode, G., Procopiuc, C. M., Srivastava, D., and Xiao, X
Cited in the paper.
Privbayes: Private data release via bayesian networks
Zhang, J., Cormode, G., Procopiuc, C. M., Srivastava, D., and Xiao, X
Cited in the paper.
Cheng, V., Suriyakumar, V. M., Dullerud, N., Joshi, S., and Ghassemi, M · 2021
Closest in time.
Us broadband coverage data set: A differentially private data release
Pereira, M., Kim, A., Allen, J., White, K., Ferres, J. L., and Dodhia, R · 2021
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Rajotte, J.-F., Mukherjee, S., Robinson, C., Ortiz, A., West, C., Ferres, J. L., and Ng, R. T · 2021
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Ffpdg: Fast, fair and private data generation
Xu, W., Zhao, J., Iannacci, F., and Wang, B · 2021
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