Origin-Destination (OD) matrices are a core component of research on users' mobility and summarize how individuals move between geographical regions. These regions should be small enough to be representative of user mobility, without incurring substantial privacy risks. There are two added values of the NetMob2025 challenge dataset. Firstly, the data is extensive and contains a lot of socio-demographic information that can be used to create multiple OD matrices, based on the segments of the population. Secondly, a participant is not merely a record in the data, but a statistically weighted proxy for a segment of the real population. This opens the door to a fundamental shift in the anonymization paradigm. A population-based view of privacy is central to our contribution. By adjusting our anonymization framework to account for representativeness, we are also protecting the inferred identity of the actual population, rather than survey participants alone. The challenge addressed in this work is to produce and compare OD matrices that are k-anonymous for survey participants and for the whole population. We compare several traditional methods of anonymization to k-anonymity by generalizing geographical areas. These include generalization over a hierarchy (ATG and OIGH) and the classical Mondrian. To this established toolkit, we add a novel method, i.e., ODkAnon, a greedy algorithm aiming at balancing speed and quality. Unlike previous approaches, which primarily address the privacy aspects of the given datasets, we aim to contribute to the generation of privacy-preserving OD matrices enriched with socio-demographic segmentation that achieves k-anonymity on the actual population.