S. Abu-nimeh, D. Nappa, X. Wang, and S. Nair, A comparison of machine learning techniques for phishing detection. In: 2nd Annual eCrime Researchers Summit, pp.60-69, 2007.

R. Agrawal and R. Shrikant, Privacy-preserving data mining, ACM SIGMOD Record, vol.29, issue.2, pp.439-450, 2000.

G. Asharov, Y. Lindell, T. Schneider, and M. Zohner, More efficient oblivious transfer extensions, J. Cryptol, vol.30, issue.3, pp.805-858, 2017.
DOI : 10.1007/s00145-016-9236-6

A. T. Azar and S. M. El-metwally, Decision tree classifiers for automated medical diagnosis, Neural Computing & Applications, vol.23, issue.7-8, pp.2387-2403, 2013.
DOI : 10.1007/s00521-012-1196-7

M. Barni, P. Failla, V. Kolesnikov, R. Lazzeretti, A. R. Sadeghi et al., Secure evaluation of private linear branching programs with medical applications, Computer Security-ESORICS 2009, vol.5789, pp.424-439, 2009.

F. Benhamouda, J. Herranz, M. Joye, and B. Libert, Efficient cryptosystems from 2 k-th power residue symbols, J. Cryptol, vol.30, issue.2, pp.519-549, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01394400

R. Bost, R. A. Popa, S. Tu, and S. Goldwasser, Machine learning classification over encrypted data, 22nd Annual Network and Distributed System Security Symposium (NDSS 2015). The Internet Society, 2015.
DOI : 10.14722/ndss.2015.23241

URL : http://eprint.iacr.org/2014/331.pdf

P. Bunn and R. Ostrovsky, Secure two-party k-means clustering, 14th ACM Conference on Computer and Communications Security, pp.486-497, 2007.
DOI : 10.1145/1315245.1315306

URL : http://www.cs.ucla.edu/~rafail/PUBLIC/87.pdf

I. Damgård, M. Geisler, and M. Krøigaard, Efficient and secure comparison for on-line auctions, Information Security and Privacy (ACISP 2007), vol.4586, pp.416-430, 2007.

I. Damgård, M. Geisler, and M. Krøigaard, Homomorphic encryption and secure comparison, Int. J. Appl. Cryptography, vol.1, issue.1, pp.22-31, 2008.

I. Damgård, M. Geisler, and M. Krøigaard, A correction to 'Efficient and secure comparison for on-line auctions', Int. J. Appl. Cryptography, vol.1, issue.4, pp.323-324, 2009.

W. Du and Z. Zhan, Building decision tree classifier on private data, IEEE Workshop on Privacy, Security, and Data Mining. Conferences in Research and Practice in Information Technology, vol.14, 2002.

Z. Erkin, M. Franz, J. Guajardo, S. Katzenbeisser, I. Lagendijk et al., Privacypreserving face recognition, Privacy Enhancing Technologies (PETS 2009), vol.5672, pp.235-253, 2009.

S. Even, O. Goldreich, and A. Lempel, A randomized protocol for signing contracts, Commun. ACM, vol.28, issue.6, pp.637-647, 1985.
DOI : 10.1007/978-1-4757-0602-4_19

T. K. Ho, The random subspace method for constructing decision forests, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.20, issue.8, pp.832-844, 1998.

P. C. Kocher, Timing attacks on implementations of Diffie-Hellman, RSA, DSS, and other systems, Advances in Cryptology-CRYPTO '96, vol.1109, pp.104-113, 1996.

M. W. Libbrecht and W. S. Noble, Machine learning applications in genetics and genomics, Nature Reviews Genetics, vol.16, issue.6, pp.321-332, 2015.
DOI : 10.1038/nrg3920

URL : http://europepmc.org/articles/pmc5204302?pdf=render

H. Y. Lin and W. G. Tzeng, An efficient solution to the millionaires' problem based on homomorphic encryption, Applied Cryptography and Network Security (ACNS 2005), vol.3531, pp.456-466, 2005.

Y. Lindell, Tutorials on the Foundations of Cryptography. Information Security and Cryptography, 2017.

Y. Lindell and B. Pinkas, Privacy preserving data mining, J. Cryptol, vol.15, issue.3, pp.177-206, 2002.

C. Liu and H. Wechsler, Gabor feature based classification using the enhanced Fisher linear discriminant model for face recognition, IEEE Trans. Image Processing, vol.11, issue.4, pp.467-476, 2002.

J. H. Min and Y. C. Lee, Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters, Expert Systems with Applications, vol.28, issue.4, pp.603-614, 2005.

P. Mohassel and Y. Zhang, SecureML: A system for scalable privacy-preserving machine learning, 2017 IEEE Symposium on Security and Privacy, pp.19-38, 2017.

M. Naor and B. Pinkas, Efficient oblivious transfer protocols, 12th Annual ACMSIAM Symposium on Discrete Algorithms, pp.448-457, 2001.

N. M. Nasrabadi, Pattern recognition and machine learning, J. Electronic Imaging, vol.16, issue.4, p.49901, 2007.

V. Nikolaenko, S. Ioannidis, U. Weinsberg, M. Joye, N. Taft et al., Privacy-preserving matrix factorization, 20th ACM Conference on Computer and Communications Security (CCS 2013), pp.801-812, 2013.

V. Nikolaenko, U. Weinsberg, S. Ioannidis, M. Joye, D. Boneh et al., Privacypreserving ridge regression on hundreds of millions of records, 2013 IEEE Symposium on Security and Privacy, pp.334-348, 2013.

M. O. Rabin, How to exchange secrets by oblivious transfer, 1981.

P. Resnick and H. R. Varian, Recommender systems, Commun. ACM, vol.40, issue.3, pp.56-58, 1997.

R. L. Rivest, L. Adleman, and M. L. Dertouzous, On data banks and privacy homomorphisms, Foundations of Secure Computation, pp.169-179, 1978.

R. K. Tai, J. P. Ma, Y. Zhao, and S. S. Chow, Privacy-preserving decision trees evaluation via linear functions, Computer Security-ESORICS 2017, Part II, vol.10493, pp.494-512, 2017.

J. Vaidya, H. Yu, and X. Jiang, Privacy-preserving SVM classification, Knowledge and Information Systems, vol.14, issue.2, pp.161-178, 2008.

T. Veugen, Improving the DGK comparison protocol, 2012 IEEE International Workshop on Information Forensics and Security, pp.49-54, 2012.

D. J. Wu, T. Feng, M. Naehrig, and K. Lauter, Privately evaluating decision trees and random forests, Proceedings on Privacy Enhancing Technologies, vol.2016, pp.335-355, 2016.

B. W. Yap, S. H. Ong, and N. H. Husain, Using data mining to improve assessment of credit worthiness via credit scoring models, Expert Systems with Applications, vol.38, issue.10, pp.13274-13283, 2011.