  The issue of ensuring privacy for users who share their personal information
has been a growing priority in a business and scientific environment where the
use of different types of data and the laws that protect it have increased in
tandem. Different technologies have been widely developed for static
publications, i.e., where the information is published only once, such as
k-anonymity and {\epsilon}-differential privacy. In the case where microdata
information is published dynamically, although established notions such as
m-invariance and {\tau}-safety already exist, developments for improving
utility remain superficial. We propose a new heuristic approach for the NP-hard
combinatorial problem of m-invariance 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, providing in some instances solutions with
ILs of 1.87, 8.5 and 1.93, while the state-of-the art methods reported ILs of
39.03, 51.84 and 57.97, respectively.
