Preserving m-privacy to Anonymize a horizontally distributed Dataset

In this paper the m-privacy verification algorithms and protocols, we can use it to anonymize a horizontally distributed dataset while preserving m-privacyFirst, we introduce the notion of m-privacy, which guarantees that the anonymized data satisfies a given privacy constraint against any group of up to m colluding data providers. Second, we present heuristic algorithms exploiting the monotonicity of privacy constraints for efficiently checking m-privacy given a group of records. Third, we present a data provider-aware anonymization algorithm with adaptive m-privacy checking strategies to ensure high utility and m-privacy of anonymized data with efficiency. Finally, we propose secure multi-party computation protocols for collaborative data publishing with m privacy. All protocols are extensively analyzed and their security and efficiency are formally proved. Experiments on real-life datasets suggest that our approach achieves better or comparable utility and efficiency than existing and baseline algorithms while satisfying m-privacy.