M. Abadi, A. Chu, I. Goodfellow, I. H-brendan-mcmahan, K. Mironov et al., Deep learning with differential privacy, Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp.308-318, 2016.

G. Acs, C. Castelluccia, and R. Chen, Differentially private histogram publishing through lossy compression, IEEE 12th International Conference on, pp.1-10, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00747821

. Ra-askey and . Ab-olde-daalhuis, Generalized hypergeometric functions and meijer g-function. NIST handbook of mathematical functions, pp.403-418, 2010.

R. Bassily, A. Groce, J. Katz, and A. Smith, Coupled-worlds privacy: Exploiting adversarial uncertainty in statistical data privacy, 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, pp.439-448, 2013.

M. Bun and T. Steinke, Concentrated differential privacy: Simplifications, extensions, and lower bounds, 2016.

K. Chaudhuri, C. Monteleoni, and A. Sarwate, Differentially private empirical risk minimization, Journal of Machine Learning Research, vol.12, pp.1069-1109, 2011.

Y. Chen, S. Chong, I. A. Kash, T. Moran, and S. Vadhan, Truthful mechanisms for agents that value privacy, ACM Transactions on Economics and Computation (TEAC), vol.4, issue.3, p.13, 2016.

D. Desfontaines and B. Pejó, Sok: Differential privacies, Proceedings on Privacy Enhancing Technologies, vol.2020, pp.288-313, 2020.

C. Dwork, K. Kenthapadi, F. Mcsherry, I. Mironov, and M. Naor, Our data, ourselves: Privacy via distributed noise generation, Eurocrypt, vol.4004, pp.486-503, 2006.

C. Dwork, F. Mcsherry, K. Nissim, and A. Smith, Calibrating noise to sensitivity in private data analysis, Theory of Cryptography Conference, pp.265-284, 2006.

C. Dwork, F. Mcsherry, K. Nissim, and A. Smith, Calibrating Noise to Sensitivity in Private Data Analysis, pp.265-284, 2006.

C. Dwork and A. Roth, The algorithmic foundations of differential privacy, Foundations and Trends® in Theoretical Computer Science, vol.9, issue.3-4, pp.211-407, 2014.

C. Dwork and . Guy-n-rothblum, , 2016.

L. Simson, J. M. Garfinkel, S. Abowd, and . Powazek, Issues encountered deploying differential privacy, 2018.

A. Ghosh and A. Roth, Selling privacy at auction, Games and Economic Behavior, vol.91, pp.334-346, 2015.

R. Hall, A. Rinaldo, and L. Wasserman, Random differential privacy, Journal of Privacy and Confidentiality, vol.4, issue.2, pp.43-59, 2012.

R. Hall, A. Rinaldo, and L. Wasserman, Differential privacy for functions and functional data, Journal of Machine Learning Research, vol.14, pp.703-727, 2013.

W. Hoeffding, Probability inequalities for sums of bounded random variables, The Collected Works of Wassily Hoeffding, pp.409-426, 1994.

J. Hsu, M. Gaboardi, A. Haeberlen, S. Khanna, A. Narayan et al., Differential privacy: An economic method for choosing epsilon, Computer Security Foundations Symposium (CSF), pp.398-410, 2014.

P. Jorion, Value at risk: The new benchmark for managing financial risk, 2000.

P. Kairouz, S. Oh, and P. Viswanath, The composition theorem for differential privacy, International conference on machine learning, pp.1376-1385, 2015.

D. Kifer and B. Lin, An axiomatic view of statistical privacy and utility, Journal of Privacy and Confidentiality, vol.4, issue.1, 2012.

D. Kifer and A. Machanavajjhala, A rigorous and customizable framework for privacy, Proceedings of the 31st ACM SIGMOD-SIGACT-SIGAI symposium on Principles of Database Systems, pp.77-88, 2012.

J. Lee and C. Clifton, How much is enough? choosing ? for differential privacy, International Conference on Information Security, pp.325-340, 2011.

K. Ligett, S. Neel, A. Roth, B. Waggoner, and S. Wu, Accuracy first: Selecting a differential privacy level for accuracy constrained erm, Advances in Neural Information Processing Systems, pp.2563-2573, 2017.

A. Machanavajjhala, D. Kifer, J. Abowd, J. Gehrke, and L. Vilhuber, Privacy: Theory meets practice on the map, IEEE 24th International Conference on, pp.277-286, 2008.

P. Massart, The tight constant in the dvoretzkykiefer-wolfowitz inequality. The annals of Probability, vol.18, pp.1269-1283, 1990.

S. Meiser, Approximate and probabilistic differential privacy definitions, IACR Cryptology ePrint Archive, p.277, 2018.

P. James, M. E. Moriarty, K. D. Branda, . Olsen, D. Nilay et al., The effects of incremental costs of smoking and obesity on health care costs among adults: a 7-year longitudinal study, Journal of Occupational and Environmental Medicine, vol.54, issue.3, pp.286-291, 2012.

K. P. Murphy, Machine Learning: A Probabilistic Perspective, 2012.

K. Nissim, S. Raskhodnikova, and A. Smith, Smooth sensitivity and sampling in private data analysis, Proceedings of the thirty-ninth annual ACM symposium on Theory of computing, pp.75-84, 2007.

N. Papernot, M. Abadi, Ú. Erlingsson, I. J. Goodfellow, and K. Talwar, Semi-supervised knowledge transfer for deep learning from private training data, 5th International Conference on Learning Representations, 2017.

N. Papernot, S. Song, I. Mironov, and A. Raghunathan, Kunal Talwar, and Úlfar Erlingsson. Scalable private learning with PATE. CoRR, 2018.

A. Papoulis and . Unnikrishna-pillai, Probability, random variables, and stochastic processes. Tata McGraw-Hill Education, 2002.

B. Pejo, Q. Tang, and G. Biczok, Together or alone: The price of privacy in collaborative learning, Proceedings on Privacy Enhancing Technologies, vol.2019, issue.2, pp.47-65, 2019.

H. William and . Press, Numerical recipes 3rd edition: The art of scientific computing, 2007.

I. P. Benjamin, F. Rubinstein, and . Aldà, Pain-free random differential privacy with sensitivity sampling, International Conference on Machine Learning, pp.2950-2959, 2017.

S. Ruggles, K. Genadek, R. Goeken, J. Grover, and M. Sobek, Integrated public use microdata series, 2015.

A. Triastcyn and B. Faltings, Federated learning with bayesian differential privacy, 2019.

J. Zhang, Z. Zhang, X. Xiao, Y. Yang, and M. Winslett, Functional mechanism: regression analysis under differential privacy, Proceedings of the VLDB Endowment, vol.5, pp.1364-1375, 2012.