FIM-Anonymizing Using Tree Structured Data

FIM-anonymizing using tree structured data study about the problem of protecting privacy in the publication of set-valued data. Considering a collection of supermarket transactional data that contains detailed information about items bought together by individuals. Even after removing all personal characteristics of the buyer, which can serve as a link to his identity, thus resulting to privacy attacks from adversaries who have partial knowledge about the set. Depending upon the point of view of the adversaries. We define a new version of the k-anonymity guarantee. Our anonymization model relies on generalization instead of suppression. We develop an algorithm which find the frequent item set. The frequent-itemsets problem is that of finding sets of items that appear in (are related to) many of the same dataset.