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Running a randomized algorithm on a subsampled dataset instead of the entire dataset amplifies differential privacy guarantees.
R \ \backslash ’enyi differential privacy of the sampled gaussian mechanism
Mironov, I., Talwar, K., and Zhang, L. (2019) · 1908
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998) · 1998
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When random sampling preserves privacy
Chaudhuri, K. and Mishra, N. (2006) · 2006
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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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Optimal client sampling for federated learning
Chen, W., Horvath, S., and Richtarik, P. (2020) · 2010
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Cho, Y. J., Wang, J., and Joshi, G. (2020) · 2010
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Hiding among the clones: A simple and nearly optimal analysis of privacy amplification by shuffling
Feldman, V., McMillan, A., and Talwar, K. (2020) · 2012
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Beyond differential privacy: Composition theorems and relational logic for f-divergences between probabilistic programs
Barthe, G. and Olmedo, F. (2013) · 2013
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The algorithmic foundations of differential privacy
Dwork, C., Roth, A., et al. (2014) · 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) · 2016
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f f -divergence inequalities
Sason, I. and Verdú, S. (2016) · 2016
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Emnist: Extending mnist to handwritten letters
Cohen, G., Afshar, S., Tapson, J., and Van Schaik, A. (2017) · 2017
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Differentially private federated learning: A client level perspective
Geyer, R. C., Klein, T., and Nabi, M. (2017) · 2017
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Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, B., Moore, E., Ramage, D., Hampson, S., and Arcas, B. A. y. (2017) · 2017
Cited alongside, same era.
Privacy amplification by subsampling: tight analyses via couplings and divergences
Balle, B., Barthe, G., and Gaboardi, M. (2018) · 2018
Cited alongside, same era.
Improving the gaussian mechanism for differential privacy: Analytical calibration and optimal denoising
Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S. (2019) · 2019
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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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Deep leakage from gradients
Zhu, L., Liu, Z., and Han, S. (2019) · 2019
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Privacy amplification via random check-ins
Balle, B., Kairouz, P., McMahan, B., Thakkar, O. D., and Thakurta, A. (2020) · 2020
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Inverting gradients - how easy is it to break privacy in federated learning?
Geiping, J., Bauermeister, H., Dröge, H., and Moeller, M. (2020) · 2020
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Balle, B. and Wang, Y.-X. (2018) · 2018
Cited alongside, same era.
Learning differentially private recurrent language models
McMahan, H. B., Ramage, D., Talwar, K., and Zhang, L. (2018) · 2018
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) · 2019
Cited alongside, same era.
Amplification by shuffling: From local to central differential privacy via anonymity
Erlingsson, Ú., Feldman, V., Mironov, I., Raghunathan, A., Talwar, K., and Thakurta, A. (2019) · 2019
Cited alongside, same era.
Exploiting unintended feature leakage in collaborative learning
Melis, L., Song, C., De Cristofaro, E., and Shmatikov, V. (2019) · 2019
Cited alongside, same era.
Shuffled model of differential privacy in federated learning
Girgis, A., Data, D., Diggavi, S., Kairouz, P., and Suresh, A. T. (2021a)
Cited in the paper.
Salem, A., Bhattacharya, A., Backes, M., Fritz, M., and Zhang, Y. (2020) · 2020
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Differentially private federated learning with shuffling and client self-sampling
Girgis, A. M., Data, D., and Diggavi, S. (2021b) · 2021
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Dopamine: Differentially private federated learning on medical data
Malekzadeh, M., Hasircioglu, B., Mital, N., Katarya, K., Ozfatura, M. E., and Gündüz, D. (2021) · 2021
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Towards flexible device participation in federated learning
Ruan, Y., Zhang, X., Liang, S.-C., and Joe-Wong, C. (2021) · 2021
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Privacy amplification for federated learning via user sampling and wireless aggregation
Seif, M., Chang, W.-T., and Tandon, R. (2021) · 2021
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