A multivariate Immune Clonal Selection Microaggregation Algorithm

K-anonymization implemented by microaggregation is a special clustering problem with minimum cardinality constraint, which has been proved to be an NP-hard combinatorial optimization problem. The existing heuristic microaggregation algorithms search the solutions in a limited solution space, so can only get local optimal solutions. The paper proposes an ICSMA (immune clonal selection microaggregation algorithm), which searches solutions in a global solution space, so can get global optimal solutions. The experimental results show that ICSMA generates anonymity tables with less information loss and lower disclosure risk as compared with MDAV algorithm.