[37] J. C. Duchi, M. I. Jordan, and M. J. Wainwright. Local privacy and statistical minimax rates. In 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, pages 429-438. IEEE, 2013.
[38] C. Dwork, F. McSherry, K. Nissim, and A. Smith. Calibrating noise to sensitivity in private data analysis. In Theory of cryptography conference, pages 265-284. Springer, 2006.
[39] C. Dwork, G. N. Rothblum, and S. Vadhan. Boosting and differential privacy. In 2010 IEEE 51st Annual Symposium on Foundations of Computer Science, pages 51-60. IEEE, 2010.
[40] U. Erlingsson, V. Feldman, I. Mironov, A. Raghunathan, K. Talwar, and A. Thakurta. Amplification by shuffling: From local to central differential privacy via anonymity. In Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms, pages 2468-2479. SIAM, 2019.
[41] F. Farokhi. Gradient sparsification can improve performance of differentially-private convex machine learning. In 2021 60th IEEE Conference on Decision and Control (CDC), pages 1695-1700. IEEE, 2021.
[42] V. Feldman, C. Guzman, and S. Vempala. Statistical query algorithms for mean vector estimation and stochastic convex optimization. In Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms, pages 1265-1277. SIAM, 2017.
[43] V. Feldman, A. McMillan, and K. Talwar. Hiding among the clones: A simple and nearly optimal analysis of privacy amplification by shuffling. In 2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS), pages 954–964. IEEE, 2022.
[44] V. Feldman, A. McMillan, and K. Talwar. Stronger privacy amplification by shuffling for Rényi and approximate differential privacy. In Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 4966-4981. SIAM, 2023.
[45] V. Feldman, J. Nelson, H. Nguyen, and K. Talwar. Private frequency estimation via projective geometry. In International Conference on Machine Learning, pages 6418-6433. PMLR, 2022.
[46] V. Feldman and K. Talwar. Lossless compression of efficient private local randomizers. arXiv preprint arXiv:2102.12099, 2021.
[47] J.-J. Fuchs. Spread representations. In 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), pages 814-817. IEEE, 2011.
[48] V. Gandikota, D. Kane, R. K. Maity, and A. Mazumdar. vqSGD: Vector quantized stochastic gradient descent, 2019.
[49] S. Ghadimi and G. Lan. Stochastic first-and zeroth-order methods for nonconvex stochastic programming. SIAM Journal on Optimization, 23(4):2341-2368, 2013.
[50] B. Ghazi, N. Golowich, R. Kumar, R. Pagh, and A. Velingker. On the power of multiple anonymous messages. arXiv preprint arXiv:1908.11358, 2019.
[51] B. Ghazi, R. Kumar, P. Manurangsi, and R. Pagh. Private counting from anonymous messages: Near-optimal accuracy with vanishing communication overhead. In International Conference on Machine Learning, pages 3505-3514. PMLR, 2020.
[52] A. Ghosh, T. Roughgarden, and M. Sundararajan. Universally utility-maximizing privacy mechanisms. SIAM Journal on Computing, 41(6):1673-1693, 2012.
[53] A. Girgis, D. Data, S. Diggavi, P. Kairouz, and A. T. Suresh. Shuffled model of differential privacy in federated learning. In International Conference on Artificial Intelligence and Statistics, pages 2521–2529. PMLR, 2021.
[54] A. M. Girgis and S. Diggavi. Multi-message shuffled privacy in federated learning. arXiv preprint arXiv:2302.11152, 2023.