Achieving <i>k</i>-anonymity privacy protection using generalization and suppression Export

@article{citeulike:3470872, abstract = {Often a data holder, such as a hospital or bank, needs to share person-specific records in such a way that the identities of the individuals who are the subjects of the data cannot be determined. One way to achieve this is to have the released records adhere to k - anonymity, which means each released record has at least ( k -1) other records in the release whose values are indistinct over those fields that appear in external data. So, k - anonymity provides privacy protection by guaranteeing that each released record will relate to at least k individuals even if the records are directly linked to external information. This paper provides a formal presentation of combining generalization and suppression to achieve k -anonymity. Generalization involves replacing (or recoding) a value with a less specific but semantically consistent value. Suppression involves not releasing a value at all. The Preferred Minimal Generalization Algorithm (MinGen), which is a theoretical algorithm presented herein, combines these techniques to provide k -anonymity protection with minimal distortion. The real-world algorithms Datafly and \&\#181;-Argus are compared to MinGen. Both Datafly and \&\#181;-Argus use heuristics to make approximations, and so, they do not always yield optimal results. It is shown that Datafly can over distort data and \&\#181;-Argus can additionally fail to provide adequate protection.}, address = {River Edge, NJ, USA}, author = {Sweeney, Latanya}, citeulike-article-id = {3470872}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=774553}, citeulike-linkout-1 = {http://dx.doi.org/10.1142/S021848850200165X}, doi = {10.1142/S021848850200165X}, issn = {0218-4885}, journal = {Int. J. Uncertain. Fuzziness Knowl.-Based Syst.}, keywords = {k-anonymity}, number = {5}, pages = {571--588}, posted-at = {2008-11-16 20:49:37}, priority = {4}, publisher = {World Scientific Publishing Co., Inc.}, title = {Achieving <i>k</i>-anonymity privacy protection using generalization and suppression}, url = {http://dx.doi.org/10.1142/S021848850200165X}, volume = {10}, year = {2002} }

TY - JOUR ID - citeulike:3470872 L3 - citeulike-article-id:3470872 N2 - Often a data holder, such as a hospital or bank, needs to share person-specific records in such a way that the identities of the individuals who are the subjects of the data cannot be determined. One way to achieve this is to have the released records adhere to k - anonymity, which means each released record has at least ( k -1) other records in the release whose values are indistinct over those fields that appear in external data. So, k - anonymity provides privacy protection by guaranteeing that each released record will relate to at least k individuals even if the records are directly linked to external information. This paper provides a formal presentation of combining generalization and suppression to achieve k -anonymity. Generalization involves replacing (or recoding) a value with a less specific but semantically consistent value. Suppression involves not releasing a value at all. The Preferred Minimal Generalization Algorithm (MinGen), which is a theoretical algorithm presented herein, combines these techniques to provide k -anonymity protection with minimal distortion. The real-world algorithms Datafly and &#181;-Argus are compared to MinGen. Both Datafly and &#181;-Argus use heuristics to make approximations, and so, they do not always yield optimal results. It is shown that Datafly can over distort data and &#181;-Argus can additionally fail to provide adequate protection. IS - 5 JF - Int. J. Uncertain. Fuzziness Knowl.-Based Syst. SN - 0218-4885 AD - River Edge, NJ, USA EP - 588 TI - Achieving <i>k</i>-anonymity privacy protection using generalization and suppression PB - World Scientific Publishing Co., Inc. VL - 10 SP - 571 KW - k-anonymity AU - Sweeney, Latanya PY - 2002/// UR - http://dx.doi.org/10.1142/S021848850200165X ER -