Heterogeneous K-Anonymization With High Utility

Among the privacy-preserving approaches that are known in the literature, k-anonymity remains the basis of more advanced models while still being useful as a stand-alone solution. Applying k-anonymity in practice, though, incurs severe loss of data utility, thus limiting its effectiveness and reliability in real-life applications and systems. However, such loss in utility does not necessarily arise from an inherent drawback of the model itself, but rather from the deficiencies of the algorithms used to implement the model. Conventional approaches rely on a methodology that publishes data in homogeneous generalized groups. An alternative modern data publishing scheme focuses on publishing the data in heterogeneous groups and achieves higher utility, while ensuring the same privacy guarantees. As conventional approaches cannot anonymize data following this heterogeneous scheme, innovative solutions are required for this purpose. Following this approach, in this paper we provide a set of algorithms that ensure high-utility k-anonymity, via solving an equivalent graph processing problem.