Databases Reference
In-Depth Information
4. J. Benaloh. Dense probabilistic encryption. In
Proceedings of the Workshop on
Selected Areas of Cryptography, pp 120-128
. Kingston, Ontario, May, 1994.
5. Christopher J.C. Burges. A tutorial on support vector machines for pattern
recognition. In
Data Mining and Knowledge Discovery
, 2(2):121-167, 1998.
6. C. Cortes and V. Vapnik.
Support-vector networks.
In
Machine Learning
,
20(3):273-297, 1995.
7. J. Shawe-Taylor and N. Cristianini. An introduction to support vector machines.
In
Cambridge University Press
.
8. J. Domingo-Ferrer. A provably secure additive and multiplicative privacy ho-
momorphism. In
Information Security Conference, pp 471-483, 2002
.
9. C. Dwork and K. Nissim. Privacy-preserving datamining on vertically parti-
tioned databases.
10. M. Freedman, K. Nissim, and B. Pinkas. E
icent private matching and set
intersection. In
EUROCRYPT, pp 1-19
, 2004.
11. B. Goethals, S. Laur, H. Lipmaa, and T. Mielikainen. On secure scalar product
computation for privacy-preserving data mining. In
Proceedings of the 7th An-
nual International Conference in Information Security and Cryptology (ICISC
2004), volume 3506 of Lecture Notes in Computer Science, pp 104-120
, Seoul,
Korea, December 2-3, 2004, Springer, Berlin Heidelberg New York, 2004.
12. O. Goldreich. Secure multi-party computation (working draft). http://www.
wisdom.weizmann.ac.il /home/oded/public html/foc.html, 1998.
13. T. Joachims. Text categorization with support vector machines: learning with
many relevant features. In
Proceedings of ECML-98, 10th European Conference
on Machine Learning, C. Nedellec and C. Rouveirol, eds., no 1398, pp 137-142
,
Chemnitz, DE, 1998. Springer, Berlin Heidelberg New York, DE.
14. J. Vaidya and C.W. Clifton. Privacy preserving association rule mining in ver-
tically partitioned data. In
Proceedings of the 8th ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining
, July 23-26, 2002, Ed-
monton, Alberta, Canada.
15. M. Kantarcioglu and C. Clifton. Privacy preserving data mining of association
rules on horizontally partitioned data. In
Transactions on Knowledge and Data
Engineering, IEEE Computer Society Press
, Los Alamitos, CA.
16. Y. LeCun, L. Botou, L. Jackel, H. Drucker, C. Cortes, J. Denker, I. Guyon,
U. Muller, E. Sackinger, P. Simard, and V. Vapnik. Learning algorithms for
classification: a comparison on handwritten digit recognition, 1995.
17. Y. Lindell and B. Pinkas. Privacy preserving data mining. In
Advances in
Cryptology - Crypto2000, Lecture Notes in Computer Science
, volume 1880,
2000.
18. D. Naccache and J. Stern. A new public key cryptosystem based on higher
residues. In
Proceedings of the 5th ACM Conference on Computer and Commu-
nication Security, pp 59-66
, San Francisco, California, United States, 1998.
19. T. Okamoto and S. Uchiyama. A new public-key cryptosystem as secure as
factoring. In
Eurocrypt'98, LNCS 1403, pp 308-318
, 1998.
20. R. Freund, F. Girosi, and E. Osuna. Training support vector machines: an
application to face detection. In
Proceedings of Computer Vision and Pattern
Recognition, pp 130-136
.
21. P. Paillier. Public-key cryptosystems based on composite degree residuosity
classes. In
Advances in Cryptography - EUROCRYPT '99, pp 223-238
, Prague,
Czech Republic, May 1999.
