Comparative study of k-anonymity algorithms for privacy preserving datamining

Abstract

Nowadays, privacy issue becomes one of the main concerns of persons among their raw data. this happens at a time, when more and more historically public information is also electronically available. when these data are linked together, they provide an electronic shadow of a person or organization that is as identifying and personal as a fingerprint even when the information contains no explicit identifiers, such as name and phone number. other distinctive data, such as birth date and zip code, often combine uniquely and can be linked to publicly available information to re-identify individuals. however, there are several kanonymity algorithms available in the literature to solve that problem such as datafly and incognito. nevertheless, their study of performances in terms of efficiency and accuracy is lacking. in this study, we compare these two k-anonymity algorithms. so that users can select which algorithm is more suitable for their data mining. the finding shows that datafly gives higher overall efficiency. comparing with incognito which gives high accuracy. consistent good performance of incognito in kanonymity has made a promising k-anonymity techniques to be used in the privacy preserving technique.