R. Agrawal and R. Srikant, Privacy-preserving data mining, ACM SIGMOD Record, vol.29, issue.2, pp.439-450, 2000.
DOI : 10.1145/335191.335438

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

K. Chaudhuri, A. D. Sarwate, and K. Sinha, A near-optimal algorithm for differentially-private principal components, Journal of Machine Learning Res, vol.14, pp.2905-2943, 2013.

C. Clifton and T. Tassa, On syntactic anonymity and differential privacy, 2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW), pp.161-183, 2013.
DOI : 10.1109/ICDEW.2013.6547433

C. Dwork, Differential Privacy, pp.1-12, 2006.
DOI : 10.1007/11787006_1

C. Dwork, G. N. 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

C. Dwork, Differential Privacy: A Survey of Results, Theory and Applications of Models of Computation, pp.1-19, 2008.
DOI : 10.1007/978-3-540-79228-4_1

C. Dwork, F. Mcsherry, K. Nissim, and A. Smith, Calibrating Noise to Sensitivity in Private Data Analysis, Proceedings of the 3rd Theory of Cryptography Conference, pp.265-284, 2006.
DOI : 10.1007/11681878_14

A. Friedman and A. Schuster, Data mining with differential privacy, Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '10, pp.493-502, 2010.
DOI : 10.1145/1835804.1835868

K. Fukunaga, Introduction to statistical pattern recognition, 1990.

G. Jagannathan, K. Pillaipakkamnatt, and R. N. Wright, A Practical Differentially Private Random Decision Tree Classifier, 2009 IEEE International Conference on Data Mining Workshops, pp.114-121, 2009.
DOI : 10.1109/ICDMW.2009.93
URL : http://www.cs.rutgers.edu/~rwright1/Publications/padm09a.pdf

M. Kantarcio?glukantarcio?glu, J. Jin, and C. Clifton, When do data mining results violate privacy?, KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp.599-604, 2004.

M. Kapralov and K. Talwar, On differentially private low rank approximation, SODA '13: Proceedings of the Twenty-Fourth Annual ACM-SIAM Symposium on Discrete Algorithms, pp.1395-1414, 2013.
DOI : 10.1137/1.9781611973105.101
URL : http://www.mit.edu/~kapralov/papers/dp.pdf

H. Kargupta, S. Datta, Q. Wang, and K. Sivakumar, On the privacy preserving properties of random data perturbation techniques, Third IEEE International Conference on Data Mining, p.99, 2003.
DOI : 10.1109/ICDM.2003.1250908

D. Kifer, A. Smith, and A. Thakurta, Private convex empirical risk minimization and high-dimensional regression, Journal of Machine Learning Research, vol.23, pp.1-41, 2012.

J. Lei, Differentially private M-estimators, Advances in Neural Information Processing Systems, pp.361-369, 2011.

N. Li, T. Li, and S. Venkatasubramanian, t-Closeness: Privacy Beyond k-Anonymity and l-Diversity, 2007 IEEE 23rd International Conference on Data Engineering, pp.106-115, 2007.
DOI : 10.1109/ICDE.2007.367856

A. Machanavajjhala, J. Gehrke, D. Kifer, and M. Venkitasubramaniam, -diversity, ICDE '06, 2006.
DOI : 10.1145/1217299.1217302

M. A. Pathak and B. Raj, Large Margin Gaussian Mixture Models with Differential Privacy, IEEE Transactions on Dependable and Secure Computing, vol.9, issue.4, pp.463-469, 2012.
DOI : 10.1109/TDSC.2012.27

F. Mcsherry and I. Mironov, Differentially private recommender systems, Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '09, pp.627-636, 2009.
DOI : 10.1145/1557019.1557090

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

S. Merugu and J. Ghosh, Privacy-preserving distributed clustering using generative models, Third IEEE International Conference on Data Mining, p.211, 2003.
DOI : 10.1109/ICDM.2003.1250922
URL : http://www.lans.ece.utexas.edu/~srujana/papers/icdm03.pdf

P. Samarati and L. Sweeney, Generalizing data to provide anonymity when disclosing information (abstract), Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems , PODS '98, 1998.
DOI : 10.1145/275487.275508

P. Samarati, Protecting respondents identities in microdata release, IEEE Transactions on Knowledge and Data Engineering, vol.13, issue.6, pp.1010-1027, 2001.
DOI : 10.1109/69.971193
URL : http://spdp.dti.unimi.it/papers/tkde_k-anonymity.pdf

J. Soria-comas and J. Domingo-ferrer, Connecting privacy models: synergies between k-anonymity, t-closeness and differential privacy, Eurostat work session on statistical data confidentiality, 2013.

J. Domingo-ferrer, On the connection between t-closeness and differential privacy for data releases, IEEE International Conference on Security and Cryptography (SECRYPT), 2013.

B. Rubinstein, P. L. Bartlett, L. Huang, and N. Taft, Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning, Journal of Privacy and Confidentiality, vol.4, issue.1, pp.65-100, 2012.

L. Sweeney, k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol.2, issue.3, pp.557-570, 2002.
DOI : 10.1109/RISP.1993.287632

D. Vu and A. Slavkovic, Differential Privacy for Clinical Trial Data: Preliminary Evaluations, 2009 IEEE International Conference on Data Mining Workshops, pp.138-143, 2009.
DOI : 10.1109/ICDMW.2009.52

B. Xi, M. Kantarcioglu, and A. Inan, Mixture of gaussian models and bayes error under differential privacy, Proceedings of the first ACM conference on Data and application security and privacy, CODASPY '11, pp.179-190, 2011.
DOI : 10.1145/1943513.1943537

X. Xiao and Y. Tao, Anatomy: simple and effective privacy preservation, VLDB '06: Proceedings of the 32nd international conference on Very large data bases, pp.139-150, 2006.

X. Xiao and Y. Tao, Output perturbation with query relaxation, Proceedings of the VLDB Endowment, vol.1, issue.1, pp.857-869, 2008.
DOI : 10.14778/1453856.1453949
URL : http://www.vldb.org/pvldb/1/1453949.pdf