Supporting materials

BV18

Victor Balcer and Salil P. Vadhan. Differential privacy on finite computers. In Anna R. Karlin, editor, 9th Innovations in Theoretical Computer Science Conference, ITCS 2018, January 11-14, 2018, Cambridge, MA, USA, volume 94 of LIPIcs, 43:1–43:21. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2018. URL: https://doi.org/10.4230/LIPIcs.ITCS.2018.43, doi:10.4230/LIPIcs.ITCS.2018.43.

BS16

Mark Bun and Thomas Steinke. Concentrated differential privacy: simplifications, extensions, and lower bounds. 2016. arXiv:1605.02065.

CKS20

Clément L. Canonne, Gautam Kamath, and Thomas Steinke. The discrete gaussian for differential privacy. In Hugo Larochelle, Marc’Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin, editors, Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual. 2020. URL: https://proceedings.neurips.cc/paper/2020/hash/b53b3a3d6ab90ce0268229151c9bde11-Abstract.html.

CR21

Mark Cesar and Ryan Rogers. Bounding, concentrating, and truncating: unifying privacy loss composition for data analytics. In Vitaly Feldman, Katrina Ligett, and Sivan Sabato, editors, Algorithmic Learning Theory, 16-19 March 2021, Virtual Conference, Worldwide, volume 132 of Proceedings of Machine Learning Research, 421–457. PMLR, 2021. URL: http://proceedings.mlr.press/v132/cesar21a.html.

DKM+06

Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor. Our data, ourselves: privacy via distributed noise generation. In Serge Vaudenay, editor, Advances in Cryptology - EUROCRYPT 2006, 25th Annual International Conference on the Theory and Applications of Cryptographic Techniques, St. Petersburg, Russia, May 28 - June 1, 2006, Proceedings, volume 4004 of Lecture Notes in Computer Science, 486–503. Springer, 2006. URL: https://doi.org/10.1007/11761679_29, doi:10.1007/11761679_29.

DMNS06

Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam D. Smith. Calibrating noise to sensitivity in private data analysis. In Shai Halevi and Tal Rabin, editors, Theory of Cryptography, Third Theory of Cryptography Conference, TCC 2006, New York, NY, USA, March 4-7, 2006, Proceedings, volume 3876 of Lecture Notes in Computer Science, 265–284. Springer, 2006. URL: https://doi.org/10.1007/11681878_14, doi:10.1007/11681878_14.

Mir12

Ilya Mironov. On significance of the least significant bits for differential privacy. In Ting Yu, George Danezis, and Virgil D. Gligor, editors, the ACM Conference on Computer and Communications Security, CCS’12, Raleigh, NC, USA, October 16-18, 2012, 650–661. ACM, 2012. URL: https://doi.org/10.1145/2382196.2382264, doi:10.1145/2382196.2382264.

RVRU16

Ryan M. Rogers, Salil P. Vadhan, Aaron Roth, and Jonathan R. Ullman. Privacy odometers and filters: pay-as-you-go composition. In Daniel D. Lee, Masashi Sugiyama, Ulrike von Luxburg, Isabelle Guyon, and Roman Garnett, editors, Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, 1921–1929. 2016. URL: https://proceedings.neurips.cc/paper/2016/hash/58c54802a9fb9526cd0923353a34a7ae-Abstract.html.

VW21

Salil P. Vadhan and Tianhao Wang. Concurrent composition of differential privacy. CoRR, 2021. URL: https://arxiv.org/abs/2105.14427, arXiv:2105.14427.