%0 Journal Article %1 rvidyabanu2010enhancing %A R.Vidyabanu, Divya Suzanne Thomas %A Nagaveni, N %D 2010 %E Das, Dr. Vinu V %J International Journal on Network Security %K classification k-Anonymization masking privacy %N 2 %P 4 %T Enhancing Privacy of Confidential Data using K Anonymization %U http://doi.searchdl.org/01.IJNS.1.2.07 %V 1 %X Recent advances in the field of data collection and related technologies have inaugurated a new era of research where existing data mining algorithms should be reconsidered from a different point of view, this of privacy preservation. Much research has been done recently on privacy preserving data mining (PPDM) based on perturbation, randomization and secure multiparty computations and more recently on anonymity including k-anonymity and l-diversity. We use the technique of k-Anonymization to de-associate sensitive attributes from the corresponding identifiers. This is done by anonymizing the linking attributes so that at least k released records match each value combination of the linking attributes. This paper proposes a k- Anonymization solution for classification. The proposed method has been implemented and evaluated using UCI repository datasets. After the k-anonymization solution is determined for the original data, classification, a data mining technique using the ID3 algorithm, is applied on both the original table and the compressed table .The accuracy of the both is compared by determining the entropy and the information gain values. Experiments show that the quality of classification can be preserved even for highly restrictive anonymity requirements.

@article{rvidyabanu2010enhancing, abstract = {Recent advances in the field of data collection and related technologies have inaugurated a new era of research where existing data mining algorithms should be reconsidered from a different point of view, this of privacy preservation. Much research has been done recently on privacy preserving data mining (PPDM) based on perturbation, randomization and secure multiparty computations and more recently on anonymity including k-anonymity and l-diversity. We use the technique of k-Anonymization to de-associate sensitive attributes from the corresponding identifiers. This is done by anonymizing the linking attributes so that at least k released records match each value combination of the linking attributes. This paper proposes a k- Anonymization solution for classification. The proposed method has been implemented and evaluated using UCI repository datasets. After the k-anonymization solution is determined for the original data, classification, a data mining technique using the ID3 algorithm, is applied on both the original table and the compressed table .The accuracy of the both is compared by determining the entropy and the information gain values. Experiments show that the quality of classification can be preserved even for highly restrictive anonymity requirements.}, added-at = {2012-10-02T08:19:24.000+0200}, author = {R.Vidyabanu, Divya Suzanne Thomas and Nagaveni, N}, biburl = {https://www.bibsonomy.org/bibtex/2647a7153c8775619413de4aaa069b1cb/ideseditor}, editor = {Das, Dr. Vinu V}, interhash = {7423d2395757eede068b87414e7af589}, intrahash = {647a7153c8775619413de4aaa069b1cb}, journal = {International Journal on Network Security}, keywords = {classification k-Anonymization masking privacy}, month = {July}, number = 2, pages = 4, timestamp = {2012-10-02T08:19:24.000+0200}, title = {Enhancing Privacy of Confidential Data using K Anonymization}, url = {http://doi.searchdl.org/01.IJNS.1.2.07}, volume = 1, year = 2010 }