An algorithm for k-anonymous microaggregation and clustering inspired by the design of distortion-optimized quantizers

We compare the performance of our novel algorithm to the state-of-the-art microaggregation algorithm MDAV, on both synthetic and standardized real data, which encompass the cases of small and large values of k. The most promising aspect of our proposed algorithm is its capability to maintain the same k-anonymity constraint, while outperforming MDAV by a significant reduction in data distortion, in all the cases considered.