[4] Daniel N. Baker and Ben Langmead. Dashing: fast and accurate genomic distances with hyperloglog. Genome Biology, 20, 2018.
[5] Ziv Bar-Yossef, T. S. Jayram, Ravi Kumar, D. Sivakumar, and Luca Trevisan. Counting distinct elements in a data stream. In Proceedings, International Workshop on Randomization and Computation (RANDOM), volume 2483, pages 1-10, 2002.
[6] Ziv Bar-Yossef, Ravi Kumar, and D. Sivakumar. Reductions in streaming algorithms, with an application to counting triangles in graphs. In Proceedings, ACM-SIAM Symposium on Discrete Algorithms (SODA), 2002.
[7] Kevin S. Beyer, Peter J. Haas, Berthold Reinwald, Yannis Sismanis, and Rainer Gemulla. On synopses for distinct-value estimation under multiset operations. In ACM SIGMOD Conference, 2007.
[8] Jeremiah Blocki, Elena Grigorescu, Tamalika Mukherjee, and Samson Zhou. How to make your approximation algorithm private: A black-box differentially-private transformation for tunable approximation algorithms of functions with low sensitivity. In APPROX/RANDOM, volume 275, pages 59:1-59:24, 2023.
[9] Jean Bolot, Nadia Fawaz, S. Muthukrishnan, Aleksandar Nikolov, and Nina Taft. Private decayed predicate sums on streams. In International Conference on Database Theory (ICDT), page 284-295, 2013.
[10] Joshua Brody and Amit Chakrabarti. A multi-round communication lower bound for gap hamming and some consequences. Proceedings, IEEE Conference on Computational Complexity (CCC), pages 358-368, 2009.
[11] Mark Bun and Thomas Steinke. Concentrated differential privacy: Simplifications, extensions, and lower bounds. In Proceedings, Theory of Cryptography Conference (TCC), volume 9985, pages 635-658, 2016.
[12] Mark Bun, Jonathan Ullman, and Salil Vadhan. Fingerprinting codes and the price of approximate differential privacy. SIAM Journal on Computing, 47(5):1888-1938, 2018.
[13] T.-H. Hubert Chan, Elaine Shi, and Dawn Song. Private and continual release of statistics. ACM Trans. Inf. Syst. Secur., 14(3):26:1-26:24, 2011.
[14] Lijie Chen, Badih Ghazi, Ravi Kumar, and Pasin Manurangsi. On distributed differential privacy and counting distinct elements. In Proceedings, Innovations in Theoretical Computer Science (ITCS), volume 185, pages 56:1-56:18, 2021.
[15] Seung Geol Choi, Dana Dachman-Soled, Mukul Kulkarni, and Arkady Yerukhimovich. Differentially-private multi-party sketching for large-scale statistics. Proceedings on Privacy Enhancing Technologies, 2020:153 - 174, 2020.
[16] Edith Cohen. Size-estimation framework with applications to transitive closure and reachability. J. Comput. Syst. Sci., 55:441-453, 1997.
[17] Anindya De. Lower bounds in differential privacy. In Proceedings, Theory of Cryptography Conference (TCC), volume 7194, pages 321-338, 2012.
[18] Sergey Denisov, H. Brendan McMahan, John Rush, Adam D. Smith, and Abhradeep Guha Thakurta. Improved differential privacy for SGD via optimal private linear operators on adaptive streams. In Advances in Neural Information Processing Systems (NeurIPS), 2022.
[19] Damien Desfontaines, Andreas Lochbihler, and David A. Basin. Cardinality estimators do not preserve privacy. Proceedings on Privacy Enhancing Technologies, 2019:26 - 46, 2018.
[20] Charles Dickens, Justin Thaler, and Daniel Ting. Order-invariant cardinality estimators are differentially private. In Advances in Neural Information Processing Systems (NeurIPS), 2022.
[21] Irit Dinur and Kobbi Nissim. Revealing information while preserving privacy. In Proceedings, ACM Symposium on Principles of Database Systems (PODS), pages 202-210, 2003.