[39] Jonathan Hehir, Daniel Ting, and Graham Cormode. Sketch-flip-merge: Mergeable sketches for private distinct counting. In Proceedings, International Conference on Machine Learning (ICML), volume 202, pages 12846–12865, 2023.
[40] Monika Henzinger, A. R. Sricharan, and Teresa Anna Steiner. Differentially private data structures under continual observation for histograms and related queries. CoRR, abs/2302.11341, 2023.
[41] Monika Henzinger, Jalaj Upadhyay, and Sarvagya Upadhyay. Almost tight error bounds on differentially private continual counting. In Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 5003-5039. SIAM, 2023.
[42] Stefan Heule, Marc Nunkesser, and Alexander Hall. Hyperloglog in practice: algorithmic engineering of a state of the art cardinality estimation algorithm. In International Conference on Extending Database Technology (EDBT), pages 683-692, 2013.
[43] Piotr Indyk and David P. Woodruff. Tight lower bounds for the distinct elements problem. In Proceedings, IEEE Symposium on Foundations of Computer Science (FOCS), pages 283-288, 2003.
[44] Palak Jain, Sofya Raskhodnikova, Satchit Sivakumar, and Adam D. Smith. The price of differential privacy under continual observation. In Proceedings, International Conference on Machine Learning (ICML), volume 202, pages 14654-14678, 2023.
[45] Prateek Jain, Pravesh Kothari, and Abhradeep Thakurta. Differentially private online learning. In Conference on Learning Theory (COLT), volume 23, pages 24.1-24.34, 2012.
[46] Daniel M. Kane, Jelani Nelson, and David P. Woodruff. An optimal algorithm for the distinct elements problem. In Proceedings, ACM Symposium on Principles of Database Systems (PODS), pages 41-52, 2010.
[47] Jure Leskovec, Anand Rajaraman, and Jeffrey D. Ullman. Mining of Massive Datasets, 2nd Ed. Cambridge University Press, 2014.
[48] Min Lyu, Dong Su, and Ninghui Li. Understanding the sparse vector technique for differential privacy. Proceedings, Very Large Databases (VLDB) Endowment, 10(6):637-648, 2017.
[49] Ahmed Metwally, Divyakant Agrawal, and Amr El Abbadi. Why go logarithmic if we can go linear?: Towards effective distinct counting of search traffic. In International Conference on Extending Database Technology (EDBT), volume 261, pages 618-629. ACM, 2008.
[50] Darakhshan Mir, S. Muthukrishnan, Aleksandar Nikolov, and Rebecca N. Wright. Pan-private algorithms via statistics on sketches. In Proceedings, ACM Symposium on Principles of Database Systems (PODS), page 37-48, 2011.
[51] Rasmus Pagh and Nina Mesing Stausholm. Efficient differentially private  $F_0$  linear sketching. In International Conference on Database Theory (ICDT), volume 186, pages 18:1-18:19, 2021.
[52] Victor Perrier, Hassan Jameel Asghar, and Dali Kaafar. Private continual release of real-valued data streams. In Network and Distributed System Security Symposium (NDSS), 2019.
[53] Alfred Rényi. On measures of entropy and information. *Berkeley Symp. on Math. Statist. and Prob.*, pages 547–561, 1961.
[54] Adam D. Smith, Shuang Song, and Abhradeep Thakurta. The Flajolet-Martin sketch itself preserves differential privacy: Private counting with minimal space. In Advances in Neural Information Processing Systems (NeurIPS), 2020.
[55] Shuang Song, Susan Little, Sanjay Mehta, Staal A. Vinterbo, and Kamalika Chaudhuri. Differentially private continual release of graph statistics. CoRR, abs/1809.02575, 2018.
[56] Rade Stanojevic, Mohamed Nabeel, and Ting Yu. Distributed cardinality estimation of set operations with differential privacy. IEEE Symposium on Privacy-Aware Computing (PAC), pages 37-48, 2017.