COAT: COnstraint-based anonymization of transactions

Abstract

Publishing transactional data about individuals in an anonymous form is increasingly required by organizations. Recent approaches
ensure that potentially identifying information cannot be used to link published transactions to individuals’ identities.
However, these approaches are inadequate to anonymize data that is both protected and practically useful in applications because
they incorporate coarse privacy requirements, do not integrate utility requirements, and tend to explore a small portion of
the solution space. In this paper, we propose the first approach for anonymizing transactional data under application-specific
privacy and utility requirements. We model such requirements as constraints, investigate how these constraints can be specified,
and propose COnstraint-based Anonymization of Transactions, an algorithm that anonymizes transactions using a flexible anonymization
scheme to meet the specified constraints. Experiments with benchmark datasets verify that COAT significantly outperforms the
current state-of-the-art algorithm in terms of data utility, while being comparable in terms of efficiency. Our approach is
also shown to be effective in preserving both privacy and utility in a real-world scenario that requires disseminating patients’
information.

KeywordsPrivacy-preserving data publication–Anonymity–Transactional data–Context-aware anonymization–Privacy constraints–Utility constraints