Review: Privacy Preserving and Sensitive Data Hiding Methods
DOI:
https://doi.org/10.53555/nncse.v2i1.511Keywords:
Privacy Preserving, Association Rules, Sensitive Rules, Minimum Support, Minimum confidenceAbstract
Privacy preserving data mining deals with hiding an individual’s sensitive identity without sacrificing the usability of data. It has become a very important area of concern but still this branch of research is in its infancy .People today have become well aware of the privacy intrusions of their sensitive data and are very reluctant to share their information. The major area of concern is that non-sensitive data even may deliver sensitive information, including personal information, facts or patterns. Several techniques of privacy preserving data mining have been proposed in literature.
References
J. Han and M. Kamber , “Data Mining: Concepts and Techniques”, 2nd ed.,The Morgan Kaufmann Series in Data Management Systems, Jim Gray, Series Editor 2006.
M. B. Malik, M. A. Ghazi and R. Ali, “Privacy Preserving Data Mining Techniques: Current Scenario and Future Prospects”, in proceedings of Third International Conference on Computer and Communication Technology, IEEE 2012.
P.Deivanai, J. Jesu Vedha Nayahi and V.Kavitha,” A Hybrid Data Anonymization integrated with Suppression for Preserving Privacy in mining multi party data” in proceedings of International Conference on Recent Trends in Information Technology, IEEE 2011.
M. Prakash, G. Singaravel, “A New Model for Privacy Preserving Sensitive Data Mining”, in proceedings of ICCCNT Coimbatore, India, IEEE 2012.
J. Liu, J. Luo and J. Z. Huang, “Rating: Privacy Preservation for Multiple Attributes with Different Sensitivity requirements”, in proceedings of 11th IEEE International Conference on Data Mining Workshops, IEEE 2011.
K. Alotaibi, V. J. Rayward-Smith, W. Wang and Beatriz de la Iglesia, “Non-linear Dimensionality Reduction for Privacy-Preserving Data Classification” in proceedings of 2012 ASE/IEEE International Conference on Social Computing and 2012 ASE/IEEE International Conference on Privacy, Security,Risk and Trust, IEEE 2012.
H. Kargupta and S. Datta, Q. Wang and K. Sivakumar, “On the Privacy Preserving Properties of Random Data Perturbation Techniques”, in proceedings of the Third IEEE International Conference on Data Mining, IEEE 2003.
E. G. Komishani and M. Abadi, “A Generalization-Based Approach for Personalized Privacy Preservation in Trajectory Data Publishing”, in proceedings of 6'th International Symposium on Telecommunications (IST'2012), IEEE 2012.
S. Lohiya and L. Ragha, “Privacy Preserving in Data Mining Using Hybrid Approach”, in proceedings of 2012 Fourth International Conference on Computational Intelligence and Communication Networks, IEEE 2012.
A. Parmar, U. P. Rao, D. R. Patel, “Blocking based approach for classification Rule hiding to Preserve the Privacy in Database” , in proceedings of International Symposium on Computer Science and Society, IEEE 2011.
Y. Lindell, B.Pinkas, “Privacy preserving data mining”, in proceedings of Journal of Cryptology, 5(3), 2000.
C. Aggarwal , P.S. Yu, “A condensation approach to privacy preserving data mining”, in proceedings of International Conference on Extending Database Technology (EDBT), pp. 183–199, 2004. 746
R. Agrawal and A. Srikant, " Privacy-preserving data mining”, in proceedings of SIGMOD00, pp. 439-450.
Evfimievski, A.Srikant, R.Agrawal, and Gehrke , "Privacy preserving mining of association rules", in proceedings of KDD02, pp. 217-228.
T. Jahan, G.Narsimha and C.V Guru Rao, “Data Perturbation and Features Selection in Preserving Privacy” in proceedings of 978-1-4673-1989-8/12, IEEE 2012.
S. Mumtaz, A. Rauf and S. Khusro, “A Distortion Based Technique for Preserving Privacy in OLAP Data Cube”, in proceedings of 978-1-61284-941-6/11/$26.00, IEEE 2011.
H.C. Huang, W.C. Fang, “Integrity Preservation and Privacy Protection for Medical Images with Histogram-Based Reversible Data Hiding”, in proceedings of 978-1-4577-0422-2/11/$26.00_c, IEEE 2011.
M. N. Kumbhar and R. Kharat, “Privacy Preserving Mining of Association Rules on horizontally and Vertically Partitioned Data: A Review Paper”, in proceedings of978-1-4673-5116-4/12/$31.00_c, IEEE 2012.
D.Karthikeswarant, V.M.Sudha, V.M.Suresh and A.J. Sultan, “A Pattern based framework for privacy preservation through Association rule Mining” in proceedings of International Conference On Advances In Engineering, Science And Management
(ICAESM -2012), IEEE 2012.
J. Vaidya and C. Clifton, “Privacy preserving association rule mining in vertically partitioned data”, in The Eighth ACM SIGKDD International conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, CA, July 2002, IEEE 2002.
Slava Kisilevich, Lior Rokach, Yuval Elovici, Bracha Shapira, “Efficient MultiDimensional Suppression for K-Anonymity”, in proceedings of IEEE Transactions on Knowledge and Data Engineering, Vol. 22, No. 3. (March 2010), pp. 334-347, IEEE 2010.
L. Sweeney, "k-Anonymity: A Model for Protecting Privacy”, in proceedings of Int'l Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 2002.
The free dictionary.Homepage on Privacy [Online]. Available: http://www.thefreedictionary.com/privacy.
A. Machanavajjhala, J.Gehrke, D. Kifer and M. Venkitasubramaniam, "I-Diversity: Privacy Beyond k-Anonymity", Proc. Int'l Con! Data Eng. (ICDE), p. 24, 2006.
G. Mathew, Z. Obradovic,” A Privacy-Preserving Framework for Distributed Clinical Decision Support”, in proceedings of 978-1-61284-852-5/11/$26.00 ©2011 IEEE.
Martin Beck and Michael Marh¨ofer,” Privacy-Preserving Data Mining Demonstrator”, in proceedings of 16th International Conference on Intelligence in Next Generation Networks, IEEE 2012.
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