A new mathematical optimization-based method for the m-invariance problem

Abstract

© The Author(s) 2025

Privacy preserving dynamic data publication aims at protecting data while simultaneously preserving its utility when the data is published dynamically. For static data (i.e., data published only once), privacy is based on concepts such as k-anonymity and {\epsilon}-differential privacy. In contrast, for dynamic data, the notions of m-invariance and {\tau}-safety are considered. However, most current approaches focus solely on guaranteeing m-invariance and {\tau}-safety without paying attention to the quality of the solution, such as maximizing utility. We propose a new heuristic approach for the NP-hard combinatorial problem of minvariance and {\tau}-safety, which is based on a mathematical optimization column generation scheme. The quality of a solution to m-invariance and {\tau}-safety can be measured by the Information Loss (IL), a value in [0, 100], the closer to 0 the better. We show that our approach improves by far current heuristics, reducing IL by more than 60% and, in some instances, by more than 95%.

Funding Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.

Peer Reviewed

Postprint (published version)