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Data Privacy in Data Engineering, the Privacy Preserving Models and Techniques in Data Mining and Data Publishing: Contemporary Affirmation of the Recent Literature

by Fuad Ali Mohammed Al-yarimi, Sonajharia Minz
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 60 - Number 3
Year of Publication: 2012
Authors: Fuad Ali Mohammed Al-yarimi, Sonajharia Minz
10.5120/9676-4103

Fuad Ali Mohammed Al-yarimi, Sonajharia Minz . Data Privacy in Data Engineering, the Privacy Preserving Models and Techniques in Data Mining and Data Publishing: Contemporary Affirmation of the Recent Literature. International Journal of Computer Applications. 60, 3 ( December 2012), 40-47. DOI=10.5120/9676-4103

@article{ 10.5120/9676-4103,
author = { Fuad Ali Mohammed Al-yarimi, Sonajharia Minz },
title = { Data Privacy in Data Engineering, the Privacy Preserving Models and Techniques in Data Mining and Data Publishing: Contemporary Affirmation of the Recent Literature },
journal = { International Journal of Computer Applications },
issue_date = { December 2012 },
volume = { 60 },
number = { 3 },
month = { December },
year = { 2012 },
issn = { 0975-8887 },
pages = { 40-47 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume60/number3/9676-4103/ },
doi = { 10.5120/9676-4103 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:05:41.772372+05:30
%A Fuad Ali Mohammed Al-yarimi
%A Sonajharia Minz
%T Data Privacy in Data Engineering, the Privacy Preserving Models and Techniques in Data Mining and Data Publishing: Contemporary Affirmation of the Recent Literature
%J International Journal of Computer Applications
%@ 0975-8887
%V 60
%N 3
%P 40-47
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Privacy preserving for data engineering methods like mining and publishing etc. , with the advancement of the rapid development of technologies like Internet and distributed computing has turned out to be one of the most important research areas of interest and has also triggered a serious issue of concern in accordance with the personal data usage in the recent times. Effective analysis result and gathering accurate data is desired by data users in specific, in contrast to the data owners who are concerned as their data contains personal information like the ones in government departments, Health insurance organizations and hospitals and data mining and warehouse utilities, where privacy is an issue to be taken rather seriously. Hence various proposals have been designated in data engineering methods publishing and mining for the purpose of preserving privacy. This paper briefs about the classification of the various privacy preserving approaches in data engineering, scans the current state of the art in lieu of preserving privacy of data, as also reviewing of the pros and cons of these specified approaches.

References
  1. Stanley R. M. Oliveira, and Osmar R. Zaïane1, "Towards Standardization in Privacy-Preserving Data Mining", In ACM SIGKDD 3rd Workshop on Data Mining Standards, 2004, pp. 7–17.
  2. G. Miklau and D. Suciu; Cryptographically enforced conditional access for xml; In WebDB, 2002
  3. Gerome Miklau and Dan Suciu; Controlling access to published data using cryptography. In VLDB, 2003
  4. Winslett et. al. The TrustBuilder Project; Publications Available from http://drl. cs. uiuc. edu/security/pubs. html
  5. S. Abiteboul, P. Kanellakis, and G. Grahne; on the representation and querying of sets of possible worlds; Theoretical Computer Science, 78:159{187, 1991
  6. Gosta Grahne and Alberto O. Mendelzon. Tableau techniques for querying information sources through global schemas; In ICDT, 1999
  7. A. Evfimievski, J. Gehrke, and R. Srikant. Limiting privacy breaches in privacy preserving data mining. In PODS, 2003
  8. Michal Bielecki and Jan Van den Bussche. Database interrogation using conjunctive queries; In ICDT, pages 259, 269, 2003
  9. Shariq Rizvi, Alberto O. Mendelzon, S. Sudarshan, and Prasan Roy; Extending query rewriting techniques for fine-grained access control. In SIGMOD Conference, 2004
  10. P. Samarati and L. Sweeney, "Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression", In Technical Report SRI-CSL-98-04, SRI Computer Science Laboratory, 1998.
  11. L. Sweeney, "k-anonymity: a model for protecting privacy", International Journal on Uncertainty, Fuzziness and Knowledgebased Systems, 2002, pp. 557-570.
  12. A. Machanavajjhala, J. Gehrke, and D. Kifer, "?-diversity: Privacy beyond k-anonymity", In Proc. of ICDE, Apr. 2006.
  13. N. Li, T. Li, and S. Venkatasubramanian, "t-Closeness: Privacy Beyond k-anonymity and ?-Diversity", In Proc. of ICDE, 2007, pp. 106-115
  14. A. Meyerson and R. Williams. "On the complexity of optimal k-anonymity", In Proceedings of PODS'04, pages 223–228, New York, NY, USA, 2004. ACM
  15. C. Aggarwal. "On k-anonymity and the curse of dimensionality", In Proceedings of VLDB'05, pages 901–909. VLDB Endowment, 2005
  16. L. Warner. "Randomized response: A survey technique for eliminating evasive answer bias," The American Statistical Association, 60(309):63–69, March 1965
  17. W. Du and Z. Zhan. Using randomized response techniques for privacy-preserving data mining. In Proc. of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 505{510, Washington, DC, USA, August 2003
  18. AGGARWAL, C. C. , AND YU, P. S. " A condensation approach to privacy preserving data mining. " Proc. of Intl. Conf. on Extending Database Technology (EDBT) (2004)
  19. Chen, K. , and Liu, L. "Privacy Preserving Data Classification with Rotation Pertubation", Proc. ICDM, 2005, pp. 589-592
  20. K. Liu, H. Kargupta, and J. Ryan, "Random projection-based multiplicative data perturbation for privacy preserving distributed data mining," IEEE Transactions on Knowledge and Data Engineering, January 2006, pp. 92–106
  21. Keke Chen, Gordon Sun, and Ling Liu. Towards attack-resilient geometric data perturbation. In Proceedings of the 2007 SIAM International Conference on Data Mining. ,April 2007.
  22. W. B. Johnson and J. Lindenstrauss, "Extensions of Lipshitz Mapping into Hilbert Space," Contemporary Math. , vol. 26,pp. 189-206, 1984
  23. Golub GH, van Loan CF (1996) Matrix computations, 3rd edn. John Hopkins University, Columbia, MD
  24. Ashwin Machanavajjhala and Johannes Gehrke; On the Efficiency of Checking Perfect Privacy; In Proceedings of the 25th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS 2006)
  25. Ashwin Machanavajjhala, Daniel Kifer, John Abowd, Johannes Gehrke, and Lars Vilhuber. Privacy: From Theory to Practice on the Map. In Proceedings of the 24th IEEE International Conference on Data Engineering (ICDE 2008), Cancun, Mexico, April 2008
  26. Daniel Kifer and J. E. Gehrke. Injecting Utility into Anonymized Datasets . In Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data (SIGMOD 2006)
  27. Ashwin Machanavajjhala, Johannes Gehrke, Daniel Kifer, and Muthu Venkitasubramaniam. l-Diversity: Privacy Beyond k-Anonymity. In Proceedings of the 22nd IEEE International Conference on Data Engineering (ICDE 2006), Atlanta, Georgia, April 2006
  28. Alin Deutsch, Yannis Papakonstantinou: Privacy in Database Publishing. ICDT 2005: 230-245
  29. Ruilin Liu; Hui Wang; , "Privacy-preserving data publishing," Data Engineering Workshops (ICDEW), 2010 IEEE 26th International Conference on , vol. , no. , pp. 305-308, 1-6 March 2010 doi: 10. 1109/ICDEW. 2010. 5452722
  30. Y. Tao, X. Xiao, J. Li, and D. Zhang, "On Anti-Corruption Privacy Preserving Publication," Proc. ICDE 2008
  31. X. Xiao and Y. Tao, "Anatomy: Simple and effective privacy preservation," Proc. VLDB 2006
  32. Rhonda Chaytor, Ke Wang, Patricia L. Brantingham: Fine-Grain Perturbation for Privacy Preserving Data Publishing. ICDM 2009: 740-745
  33. Ling Guo; Xiaowei Ying; Xintao Wu; , "On Attribute Disclosure in Randomization Based Privacy Preserving Data Publishing," Data Mining Workshops (ICDMW), 2010 IEEE International Conference on , vol. , no. , pp. 466-473, 13-13 Dec. 2010; doi: 10. 1109/ICDMW. 2010. 76
  34. W. Du and Z. Zhan, "Using randomized response techniques for privacy-preserving data mining," KDD, 2003
  35. S. Rizvi and J. Haritsa, "Maintaining data privacy in association rule mining," in VLDB, 2002
  36. L. Guo, S. Guo, and X. Wu, "Privacy preserving market basket data analysis," in PKDD, 2007
  37. L. Guo, and X. Wu, "Privacy preserving categorical data analysis with unknown distortion parameters," in Transaction on Data Privacy, 2009
  38. Z. Teng and W. Du, "Comparisons of k-anonymization and randomization schemes under linking attacks," in ICDM, 2006
  39. Z. Huang and W. Du, "Optrr: Optimizing randomized response schemes for privacy-preserving data mining," in ICDE, 2008, pp. 705–714
  40. Ninghui Li; Tiancheng Li; Venkatasubramanian, S. ; , "Closeness: A New Privacy Measure for Data Publishing," Knowledge and Data Engineering, IEEE Transactions on , vol. 22, no. 7, pp. 943-956, July 2010; doi: 10. 1109/TKDE. 2009. 139
  41. Tiancheng Li; Ninghui Li; Jian Zhang; Molloy, I. ; , "Slicing: A New Approach for Privacy Preserving Data Publishing," Knowledge and Data Engineering, IEEE Transactions on , vol. 24, no. 3, pp. 561-574, March 2012; doi: 10. 1109/TKDE. 2010. 236
  42. Yaping Li, Minghua Chen, Qiwei Li and Wei Zhang;"Enabling Multi-level Trust in Privacy Preserving Data Mining"; IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2011
  43. Kantarcioglu, M. ; Clifton, C. ; , "Privacy-preserving distributed mining of association rules on horizontally partitioned data," Knowledge and Data Engineering, IEEE Transactions on , vol. 16, no. 9, pp. 1026- 1037, Sept. 2004; doi: 10. 1109/TKDE. 2004. 45
  44. Kun Liu; Kargupta, H. ; Ryan, J. ; , "Random projection-based multiplicative data perturbation for privacy preserving distributed data mining," Knowledge and Data Engineering, IEEE Transactions on , vol. 18, no. 1, pp. 92- 106, Jan. 2006; doi: 10. 1109/TKDE. 2006. 1
Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Data publishing privacy preserving anonymity data engineering k-anonymity t-closeness l-diversity

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