Incognito: efficient full-domain K-anonymity Export

@inproceedings{citeulike:3394591, abstract = {A number of organizations publish microdata for purposes such as public health and demographic research. Although attributes that clearly identify individuals, such as Name and Social Security Number, are generally removed, these databases can sometimes be joined with other public databases on attributes such as Zipcode, Sex, and Birthdate to re-identify individuals who were supposed to remain anonymous. "Joining" attacks are made easier by the availability of other, complementary, databases over the Internet.K-anonymization is a technique that prevents joining attacks by generalizing and/or suppressing portions of the released microdata so that no individual can be uniquely distinguished from a group of size k. In this paper, we provide a practical framework for implementing one model of k-anonymization, called full-domain generalization. We introduce a set of algorithms for producing minimal full-domain generalizations, and show that these algorithms perform up to an order of magnitude faster than previous algorithms on two real-life databases.Besides full-domain generalization, numerous other models have also been proposed for k-anonymization. The second contribution in this paper is a single taxonomy that categorizes previous models and introduces some promising new alternatives.}, address = {New York, NY, USA}, author = {Lefevre, Kristen and Dewitt, David J. and Ramakrishnan, Raghu}, booktitle = {SIGMOD '05: Proceedings of the 2005 ACM SIGMOD international conference on Management of data}, citeulike-article-id = {3394591}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1066164}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1066157.1066164}, doi = {10.1145/1066157.1066164}, isbn = {1-59593-060-4}, keywords = {k-anonymity, privacy}, location = {Baltimore, Maryland}, pages = {49--60}, posted-at = {2009-02-18 08:49:13}, priority = {0}, publisher = {ACM}, title = {Incognito: efficient full-domain K-anonymity}, url = {http://dx.doi.org/10.1145/1066157.1066164}, year = {2005} }

TY - CONF ID - citeulike:3394591 L3 - citeulike-article-id:3394591 N2 - A number of organizations publish microdata for purposes such as public health and demographic research. Although attributes that clearly identify individuals, such as Name and Social Security Number, are generally removed, these databases can sometimes be joined with other public databases on attributes such as Zipcode, Sex, and Birthdate to re-identify individuals who were supposed to remain anonymous. "Joining" attacks are made easier by the availability of other, complementary, databases over the Internet.K-anonymization is a technique that prevents joining attacks by generalizing and/or suppressing portions of the released microdata so that no individual can be uniquely distinguished from a group of size k. In this paper, we provide a practical framework for implementing one model of k-anonymization, called full-domain generalization. We introduce a set of algorithms for producing minimal full-domain generalizations, and show that these algorithms perform up to an order of magnitude faster than previous algorithms on two real-life databases.Besides full-domain generalization, numerous other models have also been proposed for k-anonymization. The second contribution in this paper is a single taxonomy that categorizes previous models and introduces some promising new alternatives. CY - Baltimore, Maryland SN - 1-59593-060-4 AD - New York, NY, USA EP - 60 TI - Incognito: efficient full-domain K-anonymity PB - ACM T2 - SIGMOD '05: Proceedings of the 2005 ACM SIGMOD international conference on Management of data SP - 49 KW - k-anonymity KW - privacy AU - Lefevre, Kristen AU - Dewitt, David AU - Ramakrishnan, Raghu PY - 2005/// UR - http://dx.doi.org/10.1145/1066157.1066164 ER -