%0 Conference Paper %1 chen2005privacy %A Chen, Keke %A Liu, Ling %B Proceedings of the Fifth IEEE International Conference on Data Mining %C Washington, DC, USA %D 2005 %I IEEE Computer Society %K classification privacy privacy-preserving %P 589--592 %R 10.1109/ICDM.2005.121 %T Privacy Preserving Data Classification with Rotation Perturbation %U http://dx.doi.org/10.1109/ICDM.2005.121 %X Data perturbation techniques are one of the most popular models for privacy preserving data mining 3, 1. It is especially convenient for applications where the data owners need to export/publish the privacy-sensitive data. A data perturbation procedure can be simply described as follows. Before the data owner publishes the data, they randomly change the data in certain way to disguise the sensitive information while preserving the particular data property that is critical for building the data models. Several perturbation techniques have been proposed recently, among which the most typical ones are randomization approach 3 and condensation approach 1. %@ 0-7695-2278-5

@inproceedings{chen2005privacy, abstract = {Data perturbation techniques are one of the most popular models for privacy preserving data mining [3, 1]. It is especially convenient for applications where the data owners need to export/publish the privacy-sensitive data. A data perturbation procedure can be simply described as follows. Before the data owner publishes the data, they randomly change the data in certain way to disguise the sensitive information while preserving the particular data property that is critical for building the data models. Several perturbation techniques have been proposed recently, among which the most typical ones are randomization approach [3] and condensation approach [1].}, acmid = {1106402}, added-at = {2012-05-05T20:28:52.000+0200}, address = {Washington, DC, USA}, author = {Chen, Keke and Liu, Ling}, biburl = {https://www.bibsonomy.org/bibtex/217173e6dc70a134dbdfc2f915babbbf9/beate}, booktitle = {Proceedings of the Fifth IEEE International Conference on Data Mining}, description = {Privacy Preserving Data Classification with Rotation Perturbation}, doi = {10.1109/ICDM.2005.121}, interhash = {2902618c60c7e0f9d0e2f549b9a4f1e2}, intrahash = {17173e6dc70a134dbdfc2f915babbbf9}, isbn = {0-7695-2278-5}, keywords = {classification privacy privacy-preserving}, numpages = {4}, pages = {589--592}, publisher = {IEEE Computer Society}, series = {ICDM '05}, timestamp = {2012-05-05T20:29:30.000+0200}, title = {Privacy Preserving Data Classification with Rotation Perturbation}, url = {http://dx.doi.org/10.1109/ICDM.2005.121}, year = 2005 }