Personalized Privacy Preserving Publication of Transactional Datasets Using Concept Learning

Abstract

Abstract-In this paper we study the problem of protecting privacy in the publication of transactional data. Consider a collection of 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 links to his identity, the publication of such data is still subject to privacy attacks from adversaries who have partial knowledge about the set. Unlike previous works, we do not distinguish data as sensitive and non-sensitive, but we consider them both as potential quasi-identifiers and potential sensitive data, depending on the point of view of the adversary. We define a new version of the anonymity guarantee using concept learning. Our anonymization model relies on generalization using concept hierarchy and concept learning. The proposed algorithms are experimentally evaluated using real world datasets.