Fetching the paper…
Reading the bibliography…
New regulations and increased awareness of data privacy have led to the deployment of new and more efficient differentially private mechanisms across public institutions and industries.
A. Maurer and M. Pontil, “Empirical bernstein bounds and sample variance penalization,” 2009
2009
Earlier work this paper cites.
X. Nguyen, M. J. Wainwright, and M. I. Jordan, “Estimating divergence functionals and the likelihood ratio by convex risk minimization,” IEEE Transactions on Information Theory , vol. 56, no. 11, 2010
2010
Earlier work this paper cites.
B. K. Sriperumbudur, K. Fukumizu, A. Gretton, B. Schölkopf, and G. R. Lanckriet, “On the empirical estimation of integral probability metrics,” Electronic Journal of Statistics , vol. 6, pp. 1550–1599, 2012
2012
Earlier work this paper cites.
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola, “A kernel two-sample test,” The Journal of Machine Learning Research , vol. 13, no. 1, pp. 723–773, 2012
2012
Earlier work this paper cites.
K. Dixit, M. Jha, S. Raskhodnikova, and A. Thakurta, “Testing the Lipschitz property over product distributions with applications to data privacy,” in Theory of Cryptography Conference (TCC , 2013
2013
Earlier work this paper cites.
G. Barthe and F. Olmedo, “Beyond differential privacy: Composition theorems and relational logic for f-divergences between probabilistic programs,” in International Colloquium on Automata, Languages, and Programming . Springer, 2013
2013
Earlier work this paper cites.
A. Krishnamurthy, K. Kandasamy, B. Poczos, and L. Wasserman, “Nonparametric estimation of renyi divergence and friends,” in International Conference on Machine Learning . PMLR, 2014
2014
Earlier work this paper cites.
T. Desautels, A. Krause, and J. W. Burdick, “Parallelizing exploration-exploitation tradeoffs in gaussian process bandit optimization,” Journal of Machine Learning Research , 2014
2014
Earlier work this paper cites.
2016
Earlier work this paper cites.
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep learning with differential privacy,” in ACM SIGSAC conference on computer and communications security , 2016
2016
Earlier work this paper cites.
I. Mironov, “Rényi differential privacy,” in IEEE computer security foundations symposium (CSF) , 2017
2017
Earlier work this paper cites.
D. Golovin, B. Solnik, S. Moitra, G. Kochanski, J. Karro, and D. Sculley, “Google vizier: A service for black-box optimization,” in Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining , 2017
2017
Earlier work this paper cites.
A. C. Gilbert and A. McMillan, “Property testing for differential privacy,” in Allerton Conference on Communication, Control, and Computing , 2018
2018
Cited alongside, same era.
Z. Ding, Y. Wang, G. Wang, D. Zhang, and D. Kifer, “Detecting violations of differential privacy,” in Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security . Association for Computing Machinery, 2018, p. 475–489
2018
Cited alongside, same era.
M. Mohri, A. Rostamizadeh, and A. Talwalkar, Foundations of machine learning . MIT press, 2018
2018
Cited alongside, same era.
B. Jayaraman and D. Evans, “Evaluating differentially private machine learning in practice,” in USENIX Security Symposium , 2019
2019
Cited alongside, same era.
P. Zhao and L. Lai, “Minimax optimal estimation of kl divergence for continuous distributions,” IEEE Trans. Inf. Theor. , vol. 66, no. 12, p. 7787–7811, dec 2020
S. Rahimian, T. Orekondy, and M. Fritz, “Differential privacy defenses and sampling attacks for membership inference,” in ACM Workshop on Artificial Intelligence and Security , 2021
2021
Later among the works it cites.
M. Nasr, S. Songi, A. Thakurta, N. Papernot, and N. Carlin, “Adversary instantiation: Lower bounds for differentially private machine learning,” in 2021 IEEE Symposium on security and privacy (SP) , 2021
2021
Later among the works it cites.
2022
Later among the works it cites.
V. Doroshenko, B. Ghazi, P. Kamath, R. Kumar, and P. Manurangsi, “Connect the dots: Tighter discrete approximations of privacy loss distributions,” Proceedings on Privacy Enhancing Technologies , 2022
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
M. Jagielski, J. Ullman, and A. Oprea, “Auditing differentially private machine learning: How private is private sgd?” Advances in Neural Information Processing Systems , 2020
2020
Cited alongside, same era.
D. Chen, N. Yu, Y. Zhang, and M. Fritz, “Gan-leaks: A taxonomy of membership inference attacks against generative models,” in ACM SIGSAC Conference on Computer and Communications Security , 2020
2020
Cited alongside, same era.
M. Jagielski, J. Ullman, and A. Oprea, “Auditing differentially private machine learning: How private is private sgd?” Advances in Neural Information Processing Systems , vol. 33, pp. 22 205–22 216, 2020
2020
Cited alongside, same era.
B. Bichsel, S. Steffen, I. Bogunovic, and M. Vechev, “Dp-sniper: Black-box discovery of differential privacy violations using classifiers,” in Symposium on Security and Privacy (SP) . IEEE, 2021
2021
Cited alongside, same era.
J. Birrell, P. Dupuis, M. A. Katsoulakis, L. Rey-Bellet, and J. Wang, “Variational representations and neural network estimation of Rényi divergences,” SIAM Journal on Mathematics of Data Science , 2021
2021
Cited alongside, same era.
B. Bichsel, S. Steffen, I. Bogunovic, and M. Vechev, “Dp-sniper: Black-box discovery of differential privacy violations using classifiers,” in 2021 IEEE Symposium on Security and Privacy (SP) . IEEE, 2021, pp. 391–409
2021
Cited alongside, same era.
C. Guo, B. Karrer, K. Chaudhuri, and L. van der Maaten, “Bounding training data reconstruction in private (deep) learning,” in International Conference on Machine Learning (ICML) . PMLR, 2022
2022
Later among the works it cites.
B. Balle, G. Cherubin, and J. Hayes, “Reconstructing training data with informed adversaries,” in 43rd IEEE Symposium on Security and Privacy, SP 2022, San Francisco, CA, USA, May 22-26, 2022 . IEEE, 2022, pp. 1138–1156
2022
Later among the works it cites.
F. Lu, J. Munoz, M. Fuchs, T. LeBlond, E. V. Zaresky-Williams, E. Raff, F. Ferraro, and B. Testa, “A general framework for auditing differentially private machine learning,” in Advances in Neural Information Processing Systems , 2022
2022
Later among the works it cites.
Y. Zhu, J. Dong, and Y.-X. Wang, “Optimal accounting of differential privacy via characteristic function,” in International Conference on Artificial Intelligence and Statistics (AISTATS , 2022
2022
Later among the works it cites.
Y. Polyanskiy and Y. Wu, “Information theory: From coding to learning,” Book draft , 2022
2022
Later among the works it cites.
2023
Closest in time.