Improved Algorithms for Anonymization of Set-Valued Data

Data anonymization techniques enable publication of detailed information, while providing the privacy of sensitive information in the data against a variety of attacks. Anonymized data describes a set of possible worlds that include the original data. Generalization and suppression have been the most commonly used techniques 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 by Samarati and Sweeny [15], so that every tuple has at least (k-1) tuples identical with it. This concept was modified in [16] in order to introduce K m -anonymity, to limit the effects of the data dimensionality. This approach depends upon generalisation instead of suppression. To handle this problem two heuristic algorithms; namely the DA-algorithm and the AA-algorithm were developed by them.These alogorithms provide near optimal solutions in many cases.In this paper,we improve DA such that undesirable duplicates are not generated and using a FP-growth we display the anonymized data.We illustrate through suitable examples,the efficiency of our proposed algorithm.