[1. Sharma, A., V. Ojha. Implementation of Cryptography for Privacy Preserving Data Mining. – International Journal of Database Management Systems, Vol. 2, 2010, No 3, pp. 57-65.10.5121/ijdms.2010.2306]Search in Google Scholar
[2. Emekci, F., O. D. Sahin, D. Agrawal, A. El Abbadi. Privacy Preserving Decision Tree Learning over Multiple Parties. – Data & Knowledge Engineering, Vol. 63, 2007, pp. 348-361.10.1016/j.datak.2007.02.004]Search in Google Scholar
[3. Domingo-Ferrer, J., V. Torra. Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation. – Data Mining and Knowledge Discovery, Vol. 11, 2005, pp. 195-212.10.1007/s10618-005-0007-5]Search in Google Scholar
[4. Fung, B. C. M., K. Wang, R. Chen, P. S. Yu. Privacy-Preserving Data Publishing: A Survey of Recent Developments. – ACM Computing Surveys, Vol. 42, 2010, No 4, pp. 14-53.10.1145/1749603.1749605]Search in Google Scholar
[5. Friedman, A., A. Schuster. Data Mining with Differential Privacy. – In: KDD’10, July 2010, Washington, DC, USA, pp. 25-28.10.1145/1835804.1835868]Search in Google Scholar
[6. Patil, S. P., S. V. Badhe. Geometric Approach for Induction of Oblique Decision Tree. – International Journal of Computer Science and Information Technologies, Vol. 5, 2015, No 1, pp. 197-201.]Search in Google Scholar
[7. Kargupta, H., K. Liu, J. Ryan. Random Projection-Based Multiplicative Data Perturbation for Privacy Preserving Distributed Data Mining. – IEEE Transactions on Knowledge and Data Engineering, Vol. 18, 2006, No 1, pp. 92-106.10.1109/TKDE.2006.14]Search in Google Scholar
[8. Agrawal, R., R. Srikant. Privacy Preserving Data Mining. – In: Proc. of ACM SIGMOD, ACM, New York, 2000, pp. 439-450.10.1145/335191.335438]Search in Google Scholar
[9. Charu, A., A. Dakshi. On the Design and Quantification of Privacy Preserving Data Mining Algorithms. – In: ACM PODS’01, Santa Barbara, California, USA, 2001.]Search in Google Scholar
[10. Adam, N. R., J. C. Wortmann. Security-Control Methods for Statistical Databases: A Comparative Study. – ACM Computing Surveys, Vol. 21, 1989, No 4, pp. 515-556.10.1145/76894.76895]Search in Google Scholar
[11. Kim, J. J., W. E. Winkler. Multiplicative Noise for Masking Continuous Data. – In: Statistical Research Division, U.S. Bureau of the Census, Washington, DC, Tech. Rep. Statistics, 2003-01.]Search in Google Scholar
[12. Li, X.-B., S. Sarkar. A Tree-Based Data Perturbation Approach for Privacy-Preserving Data Mining. – IEEE Transactions on Knowledge and Data Engineering, Vol. 18, 2006, No 9, pp. 1278-1283.10.1109/TKDE.2006.136]Search in Google Scholar
[13. Kiran, P., K. S. Sathish, N. P. Kavya. A Novel Framework Using Elliptic Curve Cryptography for Extremely Secure Transmission in Distributed Privacy Preserving Data Mining. – International Journal in Advanced Computing, Vol. 3, 2012, No 2, pp. 85-92.10.5121/acij.2012.3210]Search in Google Scholar
[14. Li, L., M. Kantarcioglu, B. Thuraisingham. Privacy Preserving Decision Tree Mining from Perturbed Data. – In: Proc. of 42nd Hawaii International Conference on System Sciences, 2009, pp. 1-10.]Search in Google Scholar
[15. Govinda, K., E. Sathiyamoorthy. Privacy Preservation of a Group and Secure Data Storage in Cloud Environment. – Cybernetics and Information Technologies, Vol. 15, 2015, No 1, pp 46-54.10.1515/cait-2015-0005]Search in Google Scholar
[16. Vaidya, J., C. Clifton, M. Kantarcioglu, S. A. Patterson. Privacy-Preserving Decision Trees over Vertically Partitioned Data. – ACM Transactions on Knowledge Discovery from Data, Vol. 2, 2008, No 3, pp. 14-27.10.1145/1409620.1409624]Search in Google Scholar
[17. Kantarcioglu, M., C. Clifton. Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data. – IEEE Transactions on Knowledge and Data Engineering, Vol. 16, 2004, No 9, pp. 1026-1037.10.1109/TKDE.2004.45]Search in Google Scholar
[18. Chen, W. Privacy-Preserving of Check-in Services in MSNS Based on a Bit Matrix. – Cybernetics and Information Technologies, Vol. 15, 2015, No 2, pp. 111-118.10.1515/cait-2015-0032]Search in Google Scholar
[19. Kisilevich, S., L. Rokach, Y. Elovici, B. Shapira. Efficient Multidimensional Suppression for k-Anonymity. – IEEE Transactions on Knowledge and Data Engineering, Vol. 22, 2010, No 3, pp. 334-347.10.1109/TKDE.2009.91]Search in Google Scholar
[20. Wei, J., C. Clifton. A Secure Distributed Framework for Achieving k-Anonymity. – The VLDB Journal, Vol. 15, 2006, pp. 316-333.10.1007/s00778-006-0008-z]Search in Google Scholar