Hiding co-occurring frequent itemsets

Osman Abul
2009 Proceedings of the 2009 EDBT/ICDT Workshops on - EDBT/ICDT '09  
Knowledge hiding, hiding rules/patterns that are inferable from published data and attributed sensitive, is extensively studied in the literature in the context of frequent itemsets and association rules mining from transactional data. The research in this thread is focused mainly on developing sophisticated methods that achieve less distortion in data quality. With this work, we extend frequent itemset hiding to co-occurring frequent itemset hiding problem. Cooccurring frequent itemsets are
more » ... se itemsets that co-exist in the output of frequent itemset mining. What is different from the classical frequent hiding is the new sensitivity definition: an itemset set is sensitive if its itemsets appear altogether within the frequent itemset mining results. In other words, co-occurrence is defined with reference to the mining results but not to the raw input dataset, and thus it is a kind of meta-knowledge. Our notion of co-occurrence is also very different from association rules as itemsets in an association rule need to be frequently present in the same set of transactions, but the co-occurrence need not necessarily require the joint occurrence in the same set of transactions. * O. Abul is fully supported by TUBITAK under the grant number 108E016
doi:10.1145/1698790.1698810 dblp:conf/edbtw/Abul09 fatcat:vqjoyxe73bcc7gy3mxoq4ywcwq