S. Arora, E. Hazan, and S. Kale, The Multiplicative Weights Update Method: a Meta-Algorithm and Applications, Theory of Computing, pp.121-164, 2012.

K. Bache and M. Lichman, UCI Machine Learning Repository, 2013.

A. Beimel, K. Nissim, and U. Stemmer, Characterizing the sample complexity of private learners, Proceedings of the 4th conference on Innovations in Theoretical Computer Science, ITCS '13, pp.97-110, 2013.
DOI : 10.1145/2422436.2422450

A. Blum, C. Dwork, F. Mcsherry, and K. Nissim, Practical privacy, Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems , PODS '05, pp.128-138, 2005.
DOI : 10.1145/1065167.1065184

A. Blum, K. Ligett, R. , and A. , A learning theory approach to noninteractive database privacy, Journal of the ACM, vol.60, issue.2, 2013.
DOI : 10.1145/2450142.2450148
URL : http://www.cs.cmu.edu/afs/cs/Web/People/avrim/Papers/privacy-full.pdf

K. Chaudhuri and D. Hsu, Sample Complexity Bounds for Differentially Private Learning, Journal of Machine Learning Research, vol.19, pp.155-186, 2011.

K. Chaudhuri and C. Monteleoni, Privacy-preserving logistic regression, Conference on Neural Information Processing Systems (NIPS), pp.289-296, 2008.

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

K. Chaudhuri, A. Sarwate, and K. Sinha, Near-optimal differentially private principal components, Conference on Neural Information Processing Systems (NIPS), pp.998-1006, 2012.

K. Chaudhuri and S. A. Vinterbo, A Stability-based Validation Procedure for Differentially Private Machine Learning URL http://papers.nips.cc/paper/5014-a-stability-based-validation- procedure-for-differentially-private-machine-learning, pp.2652-2660, 2013.

J. Duchi, M. Jordan, and M. Wainwright, Local privacy and statistical minimax rates, IEEE Symposium on Foundations of Computer Science (FOCS), 2013.
DOI : 10.1109/allerton.2013.6736718
URL : http://www.cs.berkeley.edu/~jduchi/projects/DuchiJoWa13_focs.pdf

C. Dwork, F. Mcsherry, K. Nissim, and A. Smith, Calibrating noise to sensitivity in private data analysis, IACR Theory of Cryptography Conference (TCC), pp.265-284, 2006.
DOI : 10.29012/jpc.v7i3.405
URL : http://repository.cmu.edu/cgi/viewcontent.cgi?article=1139&context=jpc

C. Dwork, M. Naor, O. Reingold, G. Rothblum, and S. Vadhan, On the complexity of differentially private data release, Proceedings of the 41st annual ACM symposium on Symposium on theory of computing, STOC '09, pp.381-390, 2009.
DOI : 10.1145/1536414.1536467

C. Dwork, A. Nikolov, and K. Talwar, Using Convex Relaxations for Efficiently and Privately Releasing Marginals, Annual Symposium on Computational Geometry, SOCG'14, p.1385, 1308.
DOI : 10.1145/2582112.2582123

C. Dwork, G. Rothblum, and S. Vadhan, Boosting and Differential Privacy, 2010 IEEE 51st Annual Symposium on Foundations of Computer Science, pp.51-60, 2010.
DOI : 10.1109/FOCS.2010.12
URL : http://www.mit.edu/~rothblum/papers/pboosting.pdf

Y. Freund and R. Schapire, Game theory, on-line prediction and boosting, Proceedings of the ninth annual conference on Computational learning theory , COLT '96, pp.325-332, 1996.
DOI : 10.1145/238061.238163
URL : http://dl.acm.org/ft_gateway.cfm?id=238163&type=pdf

A. Gupta, A. Roth, and J. Ullman, Iterative Constructions and Private Data Release, IACR Theory of Cryptography Conference (TCC), pp.339-356, 2012.
DOI : 10.1007/978-3-642-28914-9_19
URL : http://www.cis.upenn.edu/~aaroth/Papers/IDCs.pdf

M. Hardt, K. Ligett, and F. Mcsherry, A Simple and Practical Algorithm for Differentially Private Data Release, Conference on Neural Information Processing Systems (NIPS), pp.2348-2356, 2012.

M. Hardt and G. N. Rothblum, A Multiplicative Weights Mechanism for Privacy-Preserving Data Analysis, 2010 IEEE 51st Annual Symposium on Foundations of Computer Science, pp.61-70, 2010.
DOI : 10.1109/FOCS.2010.85
URL : http://www.cs.princeton.edu/%7Emhardt/pub/HR10.pdf

J. Hsu, A. Roth, and J. Ullman, Differential privacy for the analyst via private equilibrium computation, Proceedings of the 45th annual ACM symposium on Symposium on theory of computing, STOC '13, pp.341-350, 2013.
DOI : 10.1145/2488608.2488651

S. P. Kasiviswanathan, H. K. Lee, K. Nissim, S. Raskhodnikova, and A. Smith, What Can We Learn Privately?, SIAM Journal on Computing, vol.40, issue.3, pp.793-826, 2011.
DOI : 10.1137/090756090
URL : http://www.cse.psu.edu/~asmith/privacy598/papers/klnrs08.pdf

M. J. Kearns, Efficient noise-tolerant learning from statistical queries, Journal of the ACM, vol.45, issue.6, pp.983-1006, 1998.
DOI : 10.1145/293347.293351
URL : http://www.cs.iastate.edu/~honavar/noise-pac.pdf

D. Kifer, A. Smith, and A. Thakurta, Private convex empirical risk minimization and high-dimensional regression, Journal of Machine Learning Research, vol.1, p.41, 2012.

C. Li, M. Hay, V. Rastogi, G. Miklau, and A. Mcgregor, Optimizing linear counting queries under differential privacy, Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems of data, PODS '10, pp.123-134, 2010.
DOI : 10.1145/1807085.1807104
URL : http://arxiv.org/pdf/0912.4742.pdf

C. Li and G. Miklau, An adaptive mechanism for accurate query answering under differential privacy, Proceedings of the VLDB Endowment, vol.5, issue.6, pp.514-525, 2012.
DOI : 10.14778/2168651.2168653
URL : http://arxiv.org/pdf/1202.3807

F. Mcsherry and K. Talwar, Mechanism Design via Differential Privacy, 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07), pp.94-103, 2007.
DOI : 10.1109/FOCS.2007.66

A. Narayanan and V. Shmatikov, Robust De-anonymization of Large Sparse Datasets, 2008 IEEE Symposium on Security and Privacy (sp 2008), pp.111-125, 2008.
DOI : 10.1109/SP.2008.33

A. Nikolov, K. Talwar, and L. Zhang, The geometry of differential privacy, Proceedings of the 45th annual ACM symposium on Symposium on theory of computing, STOC '13, pp.351-360, 2013.
DOI : 10.1145/2488608.2488652

A. Roth and T. Roughgarden, Interactive privacy via the median mechanism, Proceedings of the 42nd ACM symposium on Theory of computing, STOC '10, pp.765-774
DOI : 10.1145/1806689.1806794
URL : http://www.cs.cmu.edu/%7Ealroth/Papers/onlineprivacy.pdf

B. I. Rubinstein, P. L. Bartlett, L. Huang, N. A. Taft, and A. Smith, Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning (Nearly) Optimal Algorithms for Private Online Learning in Full-information and Bandit Settings, Journal of Privacy and Confidentiality Conference on Neural Information Processing Systems (NIPS), pp.2733-2741, 2012.

J. Ullman, Answering n 2+o(1) counting queries with differential privacy is hard, ACM SIGACT Symposium on Theory of Computing (STOC), pp.361-370, 2013.
DOI : 10.1145/2488608.2488653

J. Ullman and S. Vadhan, PCPs and the Hardness of Generating Private Synthetic Data, IACR Theory of Cryptography Conference (TCC), pp.400-416, 2011.
DOI : 10.1007/978-3-642-19571-6_24

G. Yaroslavtsev, G. Cormode, C. M. Procopiuc, and D. Srivastava, Accurate and efficient private release of datacubes and contingency tables, 2013 IEEE 29th International Conference on Data Engineering (ICDE), pp.745-756, 2013.
DOI : 10.1109/ICDE.2013.6544871

J. Zhang, G. Cormode, C. M. Procopiuc, D. Srivastava, X. et al., PrivBayes: Private Data Release via Bayesian Networks, ACM SIGMOD International Conference on Management of Data (SIGMOD), 2014.