Bun, M. and Steinke, T. Concentrated differential privacy: Simplifications, extensions, and lower bounds. In Hirt, M. and Smith, A. D. (eds.), Theory of Cryptography - 14th International Conference, TCC 2016-B, Beijing, China, October 31 - November 3, 2016, Proceedings, Part I, volume 9985 of Lecture Notes in Computer Science, pp. 635-658, 2016. doi: 10.1007/978-3-662-53641-4\24. URL https://doi.org/10.1007/978-3-662-53641-4_24.
Cardoso, A. R. and Rogers, R. Differentially private histograms under continual observation: Streaming selection into the unknown. In Camps-Valls, G., Ruiz, F. J. R., and Valera, I. (eds.), International Conference on Artificial Intelligence and Statistics, AISTATS 2022, 28-30 March 2022, Virtual Event, volume 151 of Proceedings of Machine Learning Research, pp. 2397-2419. PMLR, 2022. URL https://proceedings.mlr.press/v151/rivera-cardoso22a.html.
Chan, T.-H. H., Shi, E., and Song, D. Private and Continual Release of Statistics. ACM Transactions on Information and System Security, 14(3):26:1-26:24, November 2011. doi: 10.1145/2043621.2043626. URL https://doi.org/10.1145/2043621.2043626.
Chan, T. H. H., Li, M., Shi, E., and Xu, W. Differentially Private Continual Monitoring of Heavy Hitters from Distributed Streams. In Fischer-Hübner, S. and Wright, M. (eds.), Privacy Enhancing Technologies, Lecture Notes in Computer Science, pp. 140-159, Berlin, Heidelberg, 2012. Springer. doi: 10.1007/978-3-642-31680-7_8. URL https://doi.org/10.1007/978-3-642-31680-7_8.
Choquette-Choo, C. A., Ganesh, A., McKenna, R., McMahan, H. B., Rush, K., Thakurta, A. G., and Xu, Z. (Amplified) banded matrix factorization: A unified approach to private training, 2023. URL https://arxiv.org/abs/2306.08153. Appears at NeurIPS 2023.
Choquette-Choo, C. A., McMahan, H. B., Rush, J. K., and Thakurta, A. G. Multi-epoch matrix factorization mechanisms for private machine learning. In Krause, A., Brunskill, E., Cho, K., Engelhardt, B., Sabato, S., and Scarlett, J. (eds.), International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA, volume 202 of Proceedings of Machine Learning Research, pp. 5924-5963. PMLR, 2023. URL https://proceedings.mlr.press/v202/choquette-choo23a.html.
Denisov, S., McMahan, H. B., Rush, J., Smith, A. D., and Thakurta, A. G. Improved differential privacy for SGD via optimal private linear operators on adaptive streams. In NeurIPS, 2022. URL http://papers.nips.cc/paper_files/paper/2022/bitstream/271ec4d1a9ff5e6b81a6e21d38b1ba96-Abstract-Conference.html.
Dinur, I. and Nissim, K. Revealing information while preserving privacy. In Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, PODS '03, pp. 202-210, New York, NY, USA, June 2003. Association for Computing Machinery. doi: 10.1145/773153.773173. URL https://doi.org/10.1145/773153.773173.
Dwork, C. and Roth, A. The Algorithmic Foundations of Differential Privacy. Foundations and Trends® in Theoretical Computer Science, 9(3-4):211-407, 2013. doi: 10.1561/0400000042. URL https://doi.org/10.1561/0400000042.
Dwork, C., Naor, M., Pitassi, T., and Rothblum, G. N. Differential privacy under continual observation. In Proceedings of the forty-second ACM symposium on Theory of computing, STOC '10, pp. 715-724, New York, NY, USA, June 2010. Association for Computing Machinery. doi: 10.1145/1806689.1806787. URL https://doi.org/10.1145/1806689.1806787.