An Overview on Privacy Preserving Data Mining Methodologies

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2011 by IJETT Journal
Volume-2 Issue-2                          
Year of Publication : 2011
Authors :Umesh Kumar Singh, Bhupendra Kumar Pandya, Keerti Dixit


Umesh Kumar Singh, Bhupendra Kumar Pandya, Keerti Dixit."An Overview on Privacy Preserving Data Mining Methodologies". International Journal of Engineering Trends and Technology (IJETT),V2(2):11-15 Sep to Oct 2011. ISSN:2231-5381. Published by Seventh Sense Research Group.


Recent interest in the collection and monitoring of data using data mining technology for the purpose of security and business - related applications has raised serious concerns about privacy issues. For example, mining health care data for the detection of disease outbreaks may require analyzing clinic al records and pharmacy transaction data of many individuals over a certain area. However, releasing and gathering such diverse information belonging to different parties may violate privacy laws and event ually be a threat to civil liberties. Privacy preserving data mining strives to provide a solution to this dilemma. It aims to allow useful data patterns to be discovered without compromising privacy. This paper presents an brief overview on preserving data mining methodologies.


[1] See overview of articles by S. Oliveira at ml or k. Liu at ml
[2] For example research carried out by IBM, see http ://
[3] See for example overview up to 2004 at
[4] H.Kargupta, S.Datta, Q.Wang, and K.sivakumar,”On the privacy preserving properties of random data perturbation techniques,” in proceedings of the IEEE International conference on Data Mining, November 2003.
[5] K.Liu, H.Kargupta, and J.Ryan, “Random projection - based multiplicative data perturbation for privacy preserving distributed data mining,”IEEE Transaction on knowledge and Data Engineering [TKDE], vol.18, no.1, January 2006.
[6] K.liu, C. Giannella, and H. Kargupta,”An attacker’s view of distance preserving maps For privac y preserving distributed data mining,” in proceeding of the10th European conference on principles and practice of knowledge Discovery in Databases [PKDD’06], Berlin, Germany, September 2006.
[7] S.Guo and X.Wu,”On the use of spectral filtering for privacy preserving data mining,”in proceedings of the 21 st ACM Symposium on Applied computing, Dijon, France, April 2006.
[8] Z. Huang,W.Du, and B. Chen, “ Deriving private information from randomized data,” in proceeding of the 2005 ACM SIGMOD conference, Balt imroe, MD, June 2005.
[9] C.C Agarwal and P.S. Yu,” A condensation based approach to privacy preserving data mining,” in proceeding of the 9 th International conference on Extending Database Technology [EDBT’04, March 2004.
[10] R.J. Bayardo and R. Agrawa l, “Data privacy through optimal k - anonymization,” in proceeding of the 21 st International conference on Data Engineering [ICDE’05].
[11] R.Chi - wing, J.Li, A. W._C. Fu, and K.Wang, “[ ?,k] - anonymity: an enhanced k - anonymity model for privacy preserving d ata publishing,” in proceedings of the 12 th ACM SIGKDD International conference on knowledge Discovery and Data Mining [SIGKDD’06].
[12] A. Machanavajjhala, J.Gehrke, D. Kifer, and M.Venkitasubramaniam,” I - diversity: privacy beyond K - anonymity,” in proce eding of the 22 nd International Conference on Data Engineering [ICDE’06].
[13] N. Li and T. Li, “t - closeness: privacy beyond K - anonymity and L - diversity,” in proceeding of the 23 rd International Conference on Data Engineering [ICDE’07].

Privacy Preserving Data Mining