An Efficient Algorithm for Anonymization of Set-Valued Data and Representation Using Fp-Tree

摘要

Data anonymization techniques enable publication of detailed information, while providing the privacy ofsensitive information in the data against a variety of attacks. Anonymized data describes a set of possibleworlds that include the original data. Generalization and suppression have been the most commonly usedtechniques for achieving anonymization. Some algorithms to protect privacy in the publication of set-valued data were developed by Terrovitis et al .,[16]. The concept of k-anonymity was introduced bySamarati and Sweeny [15], so that every tuple has at least (k-1) tuples identical with it. This concept wasmodified in [16] in order to introducemk-anonymity, to limit the effects of the data dimensionality. Thisapproach depends upon generalisation instead of suppression.To handle this problem two heuristicalgorithms; namely the DA-algorithm and the AA-algorithm were developed by them.These alogorithmsprovide near optimal solutions in many cases.In this paper,we improve DA such that undesirableduplicates are not generated andwe can display the anonymized data even in the FP-Tree way.Weillustrate through suitable examples,the efficiency of our proposed algorithm.