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Individual privacy accounting enables bounding differential privacy (DP) loss individually for each participant involved in the analysis.
Rényi differential privacy of the sampled Gaussian mechanism
Mironov, I., Talwar, K., and Zhang, L. (2019) · 1908
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Privacy odometers and filters: pay-as-you-go composition
Rogers, R., Roth, A., Ullman, J., and Vadhan, S. (2016) · 1937
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An algorithm for the machine calculation of complex Fourier series
Cooley, J. W. and Tukey, J. W. (1965) · 1965
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PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals
Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., and Stanley, H. E. (13.06.2000) · 2000
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Stochastic integration and stochastic differential equations
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Calibrating noise to sensitivity in private data analysis
Dwork, C., McSherry, F., Nissim, K., and Smith, A. (2006) · 2006
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Numerical recipes 3rd edition: The art of scientific computing
Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P. (2007) · 2007
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Selling privacy at auction
Ghosh, A. and Roth, A. (2011) · 2011
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Introduction to numerical analysis
Stoer, J. and Bulirsch, R. (2013) · 2013
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The algorithmic foundations of differential privacy
Dwork, C. and Roth, A. (2014) · 2014
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Differential privacy: Now it’s getting personal
Ebadi, H., Sands, D., and Schneider, G. (2015) · 2015
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MIMIC-III, a freely accessible critical care database
Johnson, A. E., Pollard, T. J., Shen, L., Li-Wei, H. L., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L. A., and Mark, R. G. (2016) · 2016
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Rényi differential privacy
Mironov, I. (2017) · 2017
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Improving the Gaussian mechanism for differential privacy: Analytical calibration and optimal denoising
Balle, B. and Wang, Y.-X. (2018) · 2018
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Optuna: A next-generation hyperparameter optimization framework
Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M. (2019) · 2019
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Differential privacy has disparate impact on model accuracy
Bagdasaryan, E., Poursaeed, O., and Shmatikov, V. (2019) · 2019
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Multitask learning and benchmarking with clinical time series data
Harutyunyan, H., Khachatrian, H., Kale, D. C., Ver Steeg, G., and Galstyan, A. (2019) · 2019
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Privacy loss classes: The central limit theorem in differential privacy
Sommer, D. M., Meiser, S., and Mohammadi, E. (2019) · 2019
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Per-instance differential privacy
Wang, Y.-X. (2019) · 2019
Computing differential privacy guarantees for heterogeneous compositions using FFT
Koskela, A. and Honkela, A. (2021) · 2021
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Tight differential privacy for discrete-valued mechanisms and for the subsampled Gaussian mechanism using FFT
Koskela, A., Jälkö, J., Prediger, L., and Honkela, A. (2021) · 2021
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Lécuyer, M. (2021) · 2021
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Privately publishable per-instance privacy
Redberg, R. and Wang, Y.-X. (2021) · 2021
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Opacus: User-friendly differential privacy library in pytorch
Yousefpour, A., Shilov, I., Sablayrolles, A., Testuggine, D., Prasad, K., Malek, M., Nguyen, J., Ghosh, S., Bharadwaj, A., Zhao, J., et al. (2021) · 2021
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Subsampled Rényi differential privacy and analytical moments accountant
Wang, Y.-X., Balle, B., and Kasiviswanathan, S. P. (2019) · 2019
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The discrete gaussian for differential privacy
Canonne, C. L., Kamath, G., and Steinke, T. (2020) · 2020
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Individual sensitivity preprocessing for data privacy
Cummings, R. and Durfee, D. (2020) · 2020
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Computing tight differential privacy guarantees using FFT
Koskela, A., Jälkö, J., and Honkela, A. (2020) · 2020
Cited alongside, same era.
Bounded-leakage differential privacy
Ligett, K., Peale, C., and Reingold, O. (2020) · 2020
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Individual privacy accounting via a Rényi filter
Feldman, V. and Zrnic, T. (2021) · 2021
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Poisson subsampled Rényi differential privacy
Zhu, Y. and Wang, Y.-X. (2019) · 2021
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Gaussian differential privacy
Dong, J., Roth, A., and Su, W. J. (2022) · 2022
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Fully adaptive composition for gaussian differential privacy
Smith, A. and Thakurta, A. (2022) · 2022
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Fully adaptive composition in differential privacy
Whitehouse, J., Ramdas, A., Rogers, R., and Wu, Z. S. (2022) · 2022
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Per-instance privacy accounting for differentially private stochastic gradient descent
Yu, D., Kamath, G., Kulkarni, J., Yin, J., Liu, T.-Y., and Zhang, H. (2022) · 2022
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Optimal accounting of differential privacy via characteristic function
Zhu, Y., Dong, J., and Wang, Y.-X. (2022) · 2022
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Stronger privacy amplification by shuffling for rényi and approximate differential privacy
Feldman, V., McMillan, A., and Talwar, K. (2023) · 2023
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