Achieving K-Anonymity Privacy Protection Using Generalization and Suppression Export

@misc{citeulike:586694, abstract = {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...}, author = {Sweeney, L.}, citeulike-article-id = {586694}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.58.7384}, keywords = {data, k-anonymity, mining}, posted-at = {2008-11-16 19:30:40}, priority = {0}, title = {Achieving K-Anonymity Privacy Protection Using Generalization and Suppression}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.58.7384} }

TY - GEN ID - citeulike:586694 L3 - citeulike-article-id:586694 N2 - 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... TI - Achieving K-Anonymity Privacy Protection Using Generalization and Suppression KW - data KW - k-anonymity KW - mining AU - Sweeney, L UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.58.7384 ER -