1. bookVolume 17 (2017): Issue 2 (June 2017)
Journal Details
License
Format
Journal
eISSN
1314-4081
First Published
13 Mar 2012
Publication timeframe
4 times per year
Languages
English
access type Open Access

Partition Based Perturbation for Privacy Preserving Distributed Data Mining

Published Online: 26 Jun 2017
Volume & Issue: Volume 17 (2017) - Issue 2 (June 2017)
Page range: 44 - 55
Journal Details
License
Format
Journal
eISSN
1314-4081
First Published
13 Mar 2012
Publication timeframe
4 times per year
Languages
English
Abstract

Data mining on vertically or horizontally partitioned dataset has the overhead of protecting the private data. Perturbation is a technique that protects the revealing of data. This paper proposes a perturbation and anonymization technique that is performed on the vertically partitioned data. A third-party coordinator is used to partition the data recursively in various parties. The parties perturb the data by finding the mean, when the specified threshold level is reached. The perturbation maintains the statistical relationship among attributes.

Keywords

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.2010.2306Search 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.2007.02.004Search 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/s10618-005-0007-5Search 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. 14-53.10.1145/1749603.1749605Search in Google Scholar

5. Friedman, A., A. Schuster. Data Mining with Differential Privacy. – In: KDD’10, July 2010, Washington, DC, USA, pp. 25-28.10.1145/1835804.1835868Search 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.2006.14Search in Google Scholar

8. Agrawal, R., R. Srikant. Privacy Preserving Data Mining. – In: Proc. of ACM SIGMOD, ACM, New York, 2000, pp. 439-450.10.1145/335191.335438Search 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. 515-556.10.1145/76894.76895Search 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.2006.136Search 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.2012.3210Search 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-2015-0005Search 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. 14-27.10.1145/1409620.1409624Search 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.2004.45Search 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-2015-0032Search 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.2009.91Search 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-zSearch in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo