European Journal of Molecular & Clinical Medicine

Abstract

Data streams mining on transactional data is very attractive area for researcher, identical about transactional data makes compromise the privacy of individual so identical information must be removed from transactional data. For publish static transactional streams many privacy- preserving techniques proposed ,these methods are not straightforwardly applied on transactional data streams because it have different characteristics .in sliding window addition and removal of transaction leads be unsuccessful to satisfy ρ-uncertainty . to maintain ρ-uncertainty remove the items of window, due this heavy information loss we proposed algorithm which dynamicallyselect items for remove to maintain satisfy ρ- uncertainty with less information loss and continuously make satisfy p-uncertainty of slide window with suppression of anonymize sliding window , experimental shows our method is more efficient than batch existing batch processing anonymization sliding window methods