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Privacy preservation in fuzzy association rules using rough computing and DSR Cover

Privacy preservation in fuzzy association rules using rough computing and DSR

Cybernetics and Information Technologies
Volume 14 (2014): Issue 1 (March 2014)
By: A. Geetha Mary,  D. P. Acharjya and  N. Ch. S. N. Iyengar  
Open Access
|Apr 2014

References

  1. 1. Sweeney, L. Computational Disclosure Control: A Primer on Data Privacy Protection. Ph.D. Thesis. Massachusetts Institute of Technology, 2001.
    Search in Google Scholar
  2. 2. Sweeney, L. Achieving k-Anonymity Privacy Protection Using Generalization and Suppression. - International Journal on Uncertainty, Fuzziness and Knowledge-Based Systems, Vol. 10, 2002, No 5, 571-588. 10.1142/S021848850200165X
    Search in Google Scholar
  3. 3 . Machanavajjhala, A., D. Kifer, J. Gehrke, M. Venkitasubramani am. L-Diversity: Privacy Beyond K-Anonymity. - ACM Trans. Knowl. Discov. Data, Vol. 1, 2007, No 1, 1-52.10.1145/1217299.1217302
    Search in Google Scholar
  4. 4 . Tian, H., W. Zhan g. Extending l-Diversity for Better Data Anonymization. - In: 6th International Conference on Information Technology: New Generations, ITNG 2009, IEEE Xplore, 2009, 461-466. 10.1109/ITNG.2009.144
    Search in Google Scholar
  5. 5 . Li, N., T. Li, S. Venkatasubramanian. t-Closeness: Privacy Beyond k-Anonymity and l-Diversity. - In: 23rd International Conference on Data Engineering, IEEE Xplore, 2007, 106-115.
    Search in Google Scholar
  6. 6. Li u, L., M. Kantarcioglu, B.Thuraisingham. The Applicability of the Perturbation Based Privacy Preserving Data Mining for Real-World Data. - Data and Knowledge Engineering, Vol. 65, 2008, No 1, 5-21.10.1016/j.datak.2007.06.011
    Search in Google Scholar
  7. 7. Li, X., B. Sumit Sarkar. A Tree-Based Data Perturbation Approach for Privacy-Preserving Data Mining. - Knowledge and Data Engineering, Vol. 18, 2006, No 9, 1278-1283.10.1109/TKDE.2006.136
    Search in Google Scholar
  8. 8. Wu, X., Chu, Chao-Hsien Ch u, Y. Wang, F. Liu, D. Yue. Privacy Preserving Data Mining Research: Current Status and Key Issues. - In: Yong Shi, G. D. Van Albada, J. Dongarra, M. A. Peter, Eds. LNCS 4489. Springer, 2007, 762-772.
    Search in Google Scholar
  9. 9. Aggarwal, C. C., Philip S. Yu. A General Survey of Privacy Preserving Data Mining Models and Algorithms. - In: C. C. Aggarwal, Philip S. Yu., Eds. Privacy-Preserving Data Mining. Boston, Kluwer Academic Publishers, 2008, 11-52.10.1007/978-0-387-70992-5_2
    Search in Google Scholar
  10. 10. Zadeh, L. A. Fuzzy Sets. - Information and Control, Vol. 8, 1965, 338-353.10.1016/S0019-9958(65)90241-X
    Search in Google Scholar
  11. 11. Pawlak, Z. Rough Sets. - International Journal of Computer Information Science, Vol. 11, 1982, 341-356.10.1007/BF01001956
    Search in Google Scholar
  12. 12. Atanasov, K. T. Intuitionistic Fuzzy Sets. - Fuzzy Sets and Systems, Vol. 20, 1986, 87-96.10.1016/S0165-0114(86)80034-3
    Search in Google Scholar
  13. 13. Acharjya, D. P., B. K. Tripathy. Rough Sets on Fuzzy Approximation Spaces and Applications to Distributed Knowledge Systems. - International Journal of Artificial Intelligence and Soft Computing, Vol. 1, 2008, No 1, 1-14.10.1504/IJAISC.2008.021260
    Search in Google Scholar
  14. 14. Acharjya, D. P., B. K. Tripathy. Rough Sets on Intuitionistic Fuzzy Approximation Spaces and Knowledge Representation. - International Journal of Artificial Intelligence and Computational Research, Vol. 1, 2009, No 1, 29-36.10.1504/IJAISC.2008.021260
    Search in Google Scholar
  15. 15. Tripathy, B. K., D. P. Acharjya. Association Rule Granulation Using Rough Sets on Intuitionistic Fuzzy Approximation Spaces and Granular Computing. - Annals. Computer Science Series, Vol. 9, 2011, No 1, 125-144.
    Search in Google Scholar
  16. 16. Acharjya, D. P., L. Ezhilars i. A Knowledge Mining Model for Ranking Institutions Using Rough Computing with Ordering Rules and Formal Concept Analysis. - International Journal of Computer Science Issues, Vol. 8, 2011, No 2, 417-425.
    Search in Google Scholar
  17. 17. Manoj Gupta, R. C. Josh i. Privacy Preserving Fuzzy Association Rules Hiding in Quantitative Data. - International Journal of Computer Theory and Engineering, Vol. 1, October 2009, No 4, 382-388.10.7763/IJCTE.2009.V1.60
    Search in Google Scholar
  18. 18. Ah n, B. S., S. S. Ch o, C. Y. Ki m. The Integrated Methodology of Rough Set Theory and Artificial Neural Network for Business Failure Prediction. - Expert Systems with Applications, Vol. 18, 2000, 65-74.10.1016/S0957-4174(99)00053-6
    Search in Google Scholar
  19. 19. Ashlock, D. Evolutionary Computation for Modeling and Optimization. Springer, 2006.
    Search in Google Scholar
  20. 20. Tripathy, B. K., D. P. Acharjya. Knowledge Mining Using Ordering Rules and Rough Sets on Fuzzy Approximation Spaces. - International Journal of Advances in Science and Technology, Vol. 1, 2010, No 3, 41-50.
    Search in Google Scholar
  21. 21. Acharjya, D. P., Debasrita Ro y, Md. A. Rahama n. Prediction of Missing Associations Using Rough Computing and Bayesian Classification. - International Journal of Intelligent Systems and Applications, Vol. 4, 2012, No 11, 1-13.10.5815/ijisa.2012.11.01
    Search in Google Scholar
  22. 22. Pawlak, Z. Rough Sets: Theoretical Aspects of Reasoning about Data. London, Kluwer Academic Publishers, 1991.
    Search in Google Scholar
  23. 23. Agrawal, R., T. Marlin s, A. Swam i. Mining Association Rules between Sets of Items in Large Databases. - In: International Conference on Management of Data, Proceeding of ACM-SIGMOD, Washington, DC, 1993, 207-216.10.1145/170036.170072
    Search in Google Scholar
  24. 24. Hipp, J., U. Guntzer, G. Nakhaeizadeh. Algorithms for Association Rule Mining - A General Survey and Comparison. - SIGKDD Explorations, Vol. 2, 2000, No 2, 1-58.10.1145/360402.360421
    Search in Google Scholar
  25. 25. Wang, Shyu e-Lian g., Yu-Huei Le e, S. Billis, A. Jafari. Hiding Sensitive Items in Privacy Preserving Association Rule Mining. - International Conference on Systems, Man and Cybernetics, IEEE Xplore, Vol. 4, 2004, 3239-3244.
    Search in Google Scholar
  26. 26. Walcza k, B., D. L. Massar t. Rough Set Theory. - Chemometrics and Intelligent Laboratory, Vol. 47, 1999, No 1, 1-16.10.1016/S0169-7439(98)00200-7
    Search in Google Scholar
  27. 27. Saleem Durai, M. A., D. P. Acharjya, A. Kannan, N. Ch. S. N. Iyenga r. An Intelligent Knowledge Mining Model for Kidney Cancer Using Rough Set Theory. - International Journal of Bioinformatics Research and Applications, Vol. 8, 2012, No 5/6, 417-435. 10.1504/IJBRA.2012.04962523060419
    Search in Google Scholar
DOI: https://doi.org/10.2478/cait-2014-0005 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702
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Language: English
Page range: 52 - 71
Published on: Apr 9, 2014
Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Almost indiscernibility,
fuzzy proximity relation,
fuzzy approximation space,
ordering rules
Related subjects:
Computer sciences,
Information technology

© 2014 A. Geetha Mary, D. P. Acharjya, N. Ch. S. N. Iyengar, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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