In the era of the internet of things and full database interconnectivity, various sensitive information is gathered, stored and processed for every individual from many companies. Sometimes these datasets are made available to selected groups or to the internet users for further processing, yielding the term opened-data. However, if the dataset is not properly anonymized then sensitive information can be extracted, re-constructed and linked to every individual, raising the alarm on privacy issues. Thus, the protection of the privacy is a very important issue. Amongst the various techniques, K-anonymity is an efficient method to protect privacy in micro-data publishing. One of the implementations of the K-anonymity is the Mondrian algorithm, but it bears high computational and memory requirements. Here, we present our novel optimized multithreaded version of the Mondrian algorithm, which carries a linear speedup and can effectively scale up, exploiting the resources of a modern data center.