Iterative local search for preserving data privacy

Abstract

k-Anonymization is a popular approach for sharing datasets while preserving the privacy of personal and sensitive information. It ensures that each individual is indistinguishable from at least k-1 others in the anonymized dataset through data suppression or generalization, which inevitably leads to some information loss. The goal is to achieve k-anonymization with minimal information loss. This paper presents an efficient local search framework designed to address this challenge using arbitrary information loss metrics. The framework leverages anytime capabilities, allowing it to balance computation time and solution quality, thereby progressively improving the quality of the anonymized data. Our empirical evaluation shows that the proposed local search framework significantly reduces information loss compared to current state-of-the-art solutions, providing performance improvements of up to 54% and 43% w.r.t. the k-members and l-greedy heuristic solutions, the leading algorithms for large datasets. Additionally, our solution approach outperforms the Hun-garian-based solution, the best solution approach for small-size instances, by up to 4.7% on these instances.