Blocking is a well-known technique used to partition a set of records into several subsets of manageable size. The standard approach to blocking is to split the records according to the values of one or several attributes (called blocking attributes). This paper presents a new blocking method based on 2^d-trees for intelligently partitioning very large data sets for micro aggregation. A number of experiments has been carried out in order to compare our method with the most typical univariate one.