%0 Book Section %1 springerlink:10.1007/978-0-387-70992-5_2 %A Aggarwal, Charu C. %A Yu, Philip S. %B Privacy-Preserving Data Mining %D 2008 %E Aggarwal, Charu C. %E Yu, Philip S. %E Elmagarmid, Ahmed K. %I Springer US %K data-publishing privacy survey %P 11-52 %R 10.1007/978-0-387-70992-5_2 %T A General Survey of Privacy-Preserving Data Mining Models and Algorithms %U http://dx.doi.org/10.1007/978-0-387-70992-5_2 %V 34 %X In recent years, privacy-preserving data mining has been studied extensively, because of the wide proliferation of sensitive information on the internet. A number of algorithmic techniques have been designed for privacy-preserving data mining. In this paper, we provide a review of the state-of-the-art methods for privacy. We discuss methods for randomization, k -anonymization, and distributed privacy-preserving data mining. We also discuss cases in which the output of data mining applications needs to be sanitized for privacy-preservation purposes. We discuss the computational and theoretical limits associated with privacy-preservation over high dimensional data sets. %@ 978-0-387-70992-5

@incollection{springerlink:10.1007/978-0-387-70992-5_2, abstract = {In recent years, privacy-preserving data mining has been studied extensively, because of the wide proliferation of sensitive information on the internet. A number of algorithmic techniques have been designed for privacy-preserving data mining. In this paper, we provide a review of the state-of-the-art methods for privacy. We discuss methods for randomization, k -anonymization, and distributed privacy-preserving data mining. We also discuss cases in which the output of data mining applications needs to be sanitized for privacy-preservation purposes. We discuss the computational and theoretical limits associated with privacy-preservation over high dimensional data sets.}, added-at = {2012-09-06T11:13:34.000+0200}, affiliation = {IBM Thomas J. Watson Research Center 19 Skyline Drive 10532 Hawthorne NY USA}, author = {Aggarwal, Charu C. and Yu, Philip S.}, biburl = {https://www.bibsonomy.org/bibtex/25d8b14d30b027535c45229547d0d105d/matthiashuber}, booktitle = {Privacy-Preserving Data Mining}, description = {Abstract - SpringerLink}, doi = {10.1007/978-0-387-70992-5_2}, editor = {Aggarwal, Charu C. and Yu, Philip S. and Elmagarmid, Ahmed K.}, interhash = {a5d7de64fd534f3e2144c6c251305070}, intrahash = {5d8b14d30b027535c45229547d0d105d}, isbn = {978-0-387-70992-5}, keyword = {Computer Science}, keywords = {data-publishing privacy survey}, pages = {11-52}, publisher = {Springer US}, series = {The Kluwer International Series on Advances in Database Systems}, timestamp = {2012-09-06T11:24:58.000+0200}, title = {A General Survey of Privacy-Preserving Data Mining Models and Algorithms}, url = {http://dx.doi.org/10.1007/978-0-387-70992-5_2}, volume = 34, year = 2008 }