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Continuous data publishing consists in the republication of updating microdata. The most relevant syntactic notions in continuous data publishing are based on m-invariance. This notion enforces that no user can be distinguished among, at least,  m  - 1 other users, each with distinct secret data. To achieve m-invariance, the existing methods must first alter the dataset to satisfy a property called m-eligibility. Essentially, a dataset can be made m-invariant if and only if it satisfies the m-eligibility constraint. Although guaranteeing the m-eligibility property is a crucial step, no theoretical study of the best strategies to achieve it has been carried out. This paper performs such a study by giving strategies and demonstrating their optimality under two approaches: insertion of counterfeit tuples and partial publication. The empirical evaluation of our proposal shows a significant reduction on the number of modifications needed to enforce m-eligbility of up to 41% with respect to the literature.

m-Eligibility with minimum counterfeits and deletions for privacy protection in continuous data publishing