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For static data (i.e., data published only once), privacy is based on concepts such as k<\/jats:italic>-anonymity and \n \n $$\\epsilon $$<\/jats:tex-math>\n \n \u03f5<\/mml:mi>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>-differential privacy. In contrast, for dynamic data, the notions of m<\/jats:italic>-invariance and \n \n $$\\tau $$<\/jats:tex-math>\n \n \u03c4<\/mml:mi>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>-safety are considered. However, most current approaches focus solely on guaranteeing m<\/jats:italic>-invariance and \n \n $$\\tau $$<\/jats:tex-math>\n \n \u03c4<\/mml:mi>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>-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 m<\/jats:italic>-invariance and \n \n $$\\tau $$<\/jats:tex-math>\n \n \u03c4<\/mml:mi>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>-safety, which is based on a mathematical optimization column generation scheme. The quality of a solution to m<\/jats:italic>-invariance and \n \n $$\\tau $$<\/jats:tex-math>\n \n \u03c4<\/mml:mi>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>-safety can be measured by the Information Loss (IL<\/jats:italic>), a value in [0, 100], the closer to 0 the better. We show that our approach improves by far current heuristics, reducing IL<\/jats:italic> by more than \n \n $$60\\%$$<\/jats:tex-math>\n \n \n 60<\/mml:mn>\n %<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula> and, in some instances, by more than \n \n $$95\\%$$<\/jats:tex-math>\n \n \n 95<\/mml:mn>\n %<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>.<\/jats:p>","DOI":"10.1007\/s00500-025-10514-1","type":"journal-article","created":{"date-parts":[[2025,2,8]],"date-time":"2025-02-08T02:47:26Z","timestamp":1738982846000},"page":"861-873","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A new mathematical optimization-based method for the m-invariance problem"],"prefix":"10.1007","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0198-2475","authenticated-orcid":false,"given":"Adri\u00e1n","family":"Tobar Nicolau","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3573-4568","authenticated-orcid":false,"given":"Jordi","family":"Castro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3757-6188","authenticated-orcid":false,"given":"Claudio","family":"Gentile","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,8]]},"reference":[{"key":"10514_CR1","doi-asserted-by":"publisher","unstructured":"Aloise A, Hansen P, Rocha C, Santi E (2014) Column generation bounds for numerical microaggregation. 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