An Empirical Study on Privacy Preserving Data Mining

In modern years, advances in hardware expertise have lead to an increase in the competence to store and record personal data about consumers and individuals. This has lead to concerns that the personal data may be misused for a variety of purposes. In order to lighten these concerns, a number of techniques have newly been proposed in order to perform the data mining tasks in a privacy-preserving way. Privacy preserving data mining has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes. So society have become increasingly indisposed to share their data, frequently resulting in individuals either refusing to share their data or providing incorrect data. Privacy preserving data mining has been studied extensively, because of the wide explosion of sensitive information on the global source. In this paper, we provide a review of methods for privacy and analyze the representative technique for privacy preserving data

[1]  Chris Clifton,et al.  Tools for privacy preserving distributed data mining , 2002, SKDD.

[2]  Latanya Sweeney,et al.  k-Anonymity: A Model for Protecting Privacy , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[3]  Sheng Zhong,et al.  Privacy-Preserving Classification of Customer Data without Loss of Accuracy , 2005, SDM.

[4]  Taneli Mielikäinen,et al.  Cryptographically private support vector machines , 2006, KDD '06.

[5]  Chris Clifton,et al.  SECURITY AND PRIVACY IMPLICATIONS OF DATA MINING , 1996 .

[6]  Vassilios S. Verykios,et al.  Disclosure limitation of sensitive rules , 1999, Proceedings 1999 Workshop on Knowledge and Data Engineering Exchange (KDEX'99) (Cat. No.PR00453).

[7]  Yehuda Lindell,et al.  Privacy Preserving Data Mining , 2002, Journal of Cryptology.

[8]  James A. Landay,et al.  An architecture for privacy-sensitive ubiquitous computing , 2004, MobiSys '04.

[9]  Philip S. Yu,et al.  Template-based privacy preservation in classification problems , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[10]  Ira S. Moskowitz,et al.  Parsimonious downgrading and decision trees applied to the inference problem , 1998, NSPW '98.

[11]  Alexandre V. Evfimievski,et al.  Privacy preserving mining of association rules , 2002, Inf. Syst..

[12]  Charu C. Aggarwal,et al.  On the design and quantification of privacy preserving data mining algorithms , 2001, PODS.

[13]  Chris Clifton,et al.  Privacy-preserving distributed mining of association rules on horizontally partitioned data , 2004, IEEE Transactions on Knowledge and Data Engineering.