C. Charu and . Aggarwal, On k-anonymity and the curse of dimensionality, Proceedings of the 31st international conference on Very large data bases VLDB, pp.901-909, 2005.

G. Aggarwal, T. Feder, K. Kenthapadi, R. Motwani, and R. Panigrahy, Approximation algorithms for k-anonymity, Dilys Thomas, and An Zhu, 2005.

D. Brain and G. Webb, On the effect of data set size on bias and variance in classification learning, Proceedings of the Fourth Australian Knowledge Acquisition Workshop, pp.117-128, 1999.

A. Campan and T. M. Truta, Data and Structural k-Anonymity in Social Networks, Privacy, Security, and Trust in KDD, pp.33-54, 2009.
DOI : 10.1109/ICDE.2008.4497459
URL : http://www.nku.edu/~trutat1/papers/LNCS09_campan.pdf

C. John, . Duchi, I. Michael, . Jordan, J. Martin et al., Privacy aware learning, Journal of the ACM (JACM), issue.6, p.6138, 2014.

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

A. Holzinger, . Plass, . Holzinger, . Gc-crisan, V. Pintea et al., Towards interactive Machine Learning (iML): Applying Ant Colony Algorithms to Solve the Traveling Salesman Problem with the Human-in-the-Loop Approach, IFIP International Cross Domain Conference and Workshop (CD-ARES), page in print, 2016.
DOI : 10.1016/j.compedu.2005.11.026
URL : https://hal.archives-ouvertes.fr/hal-01635020

A. Holzinger, Interactive machine learning for health informatics: when do we need the human-in-the-loop?, Brain Informatics, vol.7, issue.1, pp.119-131, 2016.
DOI : 10.1007/978-3-540-69868-5_1

A. Holzinger, Introduction to MAchine Learning & Knowledge Extraction (MAKE), Machine Learning and Knowledge Extraction, vol.15, issue.1, pp.1-20, 2017.
DOI : 10.1007/978-3-642-40511-2_22

P. Kieseberg, B. Malle, P. Frhwirt, E. Weippl, and A. Holzinger, A tamper-proof audit and control system for the doctor in the loop, Brain Informatics, vol.53, issue.4, pp.1-11, 2016.
DOI : 10.1109/ARES.2012.50

H. Lee, S. Kim, J. W. Kim, and Y. D. Chung, Utilitypreserving anonymization for health data publishing, BMC Medical Informatics and Decision Making, 2017.
DOI : 10.1186/s12911-017-0499-0
URL : https://doi.org/10.1186/s12911-017-0499-0

K. Lefevre, J. David, R. Dewitt, and . Ramakrishnan, Mondrian Multidimensional K-Anonymity, 22nd International Conference on Data Engineering (ICDE'06), pp.25-25, 2006.
DOI : 10.1109/ICDE.2006.101

J. Li, J. Liu, M. Baig, and R. Wong, Information based data anonymization for classification utility, Data & Knowledge Engineering, vol.70, issue.12, pp.1030-1045, 2011.
DOI : 10.1016/j.datak.2011.07.001

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, D. Kifer, J. Gehrke, and M. Venkitasubramaniam, -diversity, ACM Transactions on Knowledge Discovery from Data, vol.1, issue.1, pp.1-52, 2007.
DOI : 10.1145/1217299.1217302

A. Majeed, S. Ullah, and . Lee, Vulnerability- and Diversity-Aware Anonymization of Personally Identifiable Information for Improving User Privacy and Utility of Publishing Data, Sensors, vol.17, issue.5, pp.1-23, 2017.
DOI : 10.1145/1656274.1656278

B. Malle, P. Kieseberg, E. Weippl, and A. Holzinger, The Right to Be Forgotten: Towards Machine Learning on Perturbed Knowledge Bases, International Conference on Availability, Reliability, and Security, pp.251-266, 2016.
DOI : 10.1145/1540276.1540279
URL : https://hal.archives-ouvertes.fr/hal-01635002

M. E. Nergiz and C. Clifton, ??-Presence without Complete World Knowledge, IEEE Transactions on Knowledge and Data Engineering, vol.22, issue.6, pp.868-883, 2010.
DOI : 10.1109/TKDE.2009.125

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

H. Edward and . Simpson, Measurement of diversity, Nature, 1949.

L. Sweeney, ACHIEVING k-ANONYMITY PRIVACY PROTECTION USING GENERALIZATION AND SUPPRESSION, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol.2, issue.3, pp.571-588, 2002.
DOI : 10.1142/S021848850200165X

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

H. Wimmer and L. Powell, A Comparison of the Effects of K-Anonymity on Machine Learning Algorithms, International Journal of Advanced Computer Science and Applications, vol.5, issue.11, pp.1-9, 2014.
DOI : 10.14569/IJACSA.2014.051126

C. Sebastien, A. Wong, V. Gatt, . Stamatescu, D. Mark et al., Understanding data augmentation for classification: when to warp?, Digital Image Computing: Techniques and Applications (DICTA), 2016 International Conference on, pp.1-6, 2016.