[1] Martin Abadi, Andy Chu, Ian Goodfellow, Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. Deep learning with differential privacy. In Proceedings of the 23rd Annual ACM Conference on Computer and Communications Security (CCS), pages 308-318, 2016.
[2] Naman Agarwal, Ananda Theertha Suresh, Felix Xinnan X Yu, Sanjiv Kumar, and Brendan McMahan. cpgsd: Communication-efficient and differentially-private distributed sgd. In Proceedings of the 31nd Annual Conference on Advances in Neural Information Processing Systems (NeurIPS), 2018.
[3] Nir Ailon and Bernard Chazelle. The fast Johnson-Lindenstrauss transform and approximate nearest neighbors. SIAM Journal on Computing, 39(1):302-322, 2009.
[4] Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnovic. Qsgd: Communication-efficient sgd via gradient quantization and encoding. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 30, pages 1709-1720. Curran Associates, Inc., 2017. URL https://proceedings.neurips.cc/paper/2017/file/6c340f25839e6acdc73414517203f5f0-Paper.pdf.
[5] N. Alon and J. H. Spencer. The Probabilistic Method. Wiley-Interscience, second edition, 2000.
[6] Hilal Asi, John Duchi, Alireza Fallah, Omid Javidbakht, and Kunal Talwar. Private adaptive gradient methods for convex optimization. In Proceedings of the 38th International Conference on Machine Learning (ICML), pages 383-392, 2021.
[7] Hilal Asi, Vitaly Feldman, and Kunal Talwar. Optimal algorithms for mean estimation under local differential privacy. In Proceedings of the 39th International Conference on Machine Learning (ICML), 2022.
[8] Borja Balle and Yu-Xiang Wang. Improving the gaussian mechanism for differential privacy: Analytical calibration and optimal denoising. In Proceedings of the 35th International Conference on Machine Learning (ICML), pages 394-403. PMLR, 2018.
[9] Abhishek Bhowmick, John Duchi, Julien Freudiger, Gaurav Kapoor, and Ryan Rogers. Protection against reconstruction and its applications in private federated learning. arXiv:1812.00984 [stat.ML], 2018.
[10] Andrea Bittau, Ülfar Erlingsson, Petros Maniatis, Ilya Mironov, Ananth Raghunathan, David Lie, Mitch Rudominer, Ushasree Kode, Julien Tinnes, and Bernhard Seefeld. Prochlo: Strong privacy for analytics in the crowd. In Proceedings of the 26th Symposium on Operating Systems Principles, SOSP '17, page 441-459, New York, NY, USA, 2017. Association for Computing Machinery. ISBN 9781450350853. doi: 10.1145/3132747.3132769. URL https://doi.org/10.1145/3132747.3132769.
[11] Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth. Practical secure aggregation for privacy-preserving machine learning. In Proceedings of the Annual ACM SIGSAC Conference on Computer and Communications Security (CCS), pages 1175–1191, 2017.
[12] Kamalika Chaudhuri, Claire Monteleoni, and Anand D. Sarwate. Differentially private empirical risk minimization. Journal of Machine Learning Research, 12:1069-1109, 2011.
[13] Kamalika Chaudhuri, Chuan Guo, and Mike Rabbat. Privacy-aware compression for federated data analysis. In Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, volume 180, pages 296-306. PMLR, 2022. URL https://proceedings.mlrpress/v180/chaudhuri22a.html.