11. References
1.Gopi, Sivakanth, Yin Tat Lee, and Lukas Wutschitz. ”Numerical
composition of differential privacy.” Advances in Neural Information
Processing Systems 34 (2021): 11631-11642.
2.Murtagh, Jack, and Salil Vadhan. ”The complexity of computing the
optimal composition of differential privacy.” Theory of Cryptography
Conference. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015.
3.Lyu, Xin. ”Composition theorems for interactive differential privacy.”
Advances in Neural Information Processing Systems 35 (2022): 9700-9712.
4.Dong, Jinshuo, David Durfee, and Ryan Rogers. ”Optimal differential
privacy composition for exponential mechanisms.” International
Conference on Machine Learning. PMLR, 2020.
5.Wang, Hua, et al. ”Analytical composition of differential privacy via
the edgeworth accountant.” arXiv preprint arXiv:2206.04236 (2022).
6.Murtagh, Jack, and Salil Vadhan. ”The complexity of computing the
optimal composition of differential privacy.” Theory of Cryptography
Conference. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015.
7.Mironov, Ilya. ”Rényi differential privacy.” 2017 IEEE 30th computer
security foundations symposium (CSF). IEEE, 2017.
8.Liu, Yi, et al. ”Identification, amplification and measurement: A
bridge to gaussian differential privacy.” Advances in Neural Information
Processing Systems 35 (2022): 11410-11422.
9.Gopi, Sivakanth, Yin Tat Lee, and Lukas Wutschitz. ”Numerical
composition of differential privacy.” Advances in Neural Information
Processing Systems 34 (2021): 11631-11642.
10.Cai, T. Tony, Yichen Wang, and Linjun Zhang. ”The cost of privacy:
Optimal rates of convergence for parameter estimation with differential
privacy.” The Annals of Statistics 49.5 (2021): 2825-2850.
11.Geng, Quan, and Pramod Viswanath. ”Optimal noise adding mechanisms
for approximate differential privacy.” IEEE Transactions on Information
Theory 62.2 (2015): 952-969.
12.Asoodeh, Shahab, et al. ”Three variants of differential privacy:
Lossless conversion and applications.” IEEE Journal on Selected Areas in
Information Theory 2.1 (2021): 208-222.
13.Meiser, Sebastian, and Esfandiar Mohammadi. ”Tight on budget? tight
bounds for r-fold approximate differential privacy.” Proceedings of the
2018 ACM SIGSAC Conference on Computer and Communications Security.
2018.
14.Zhu, Tianqing, et al. ”Preliminary of differential privacy.”
Differential privacy and applications (2017): 7-16.