  Microaggregation is a method to coarsen a dataset, by optimally clustering
data points in groups of at least $k$ points, thereby providing a $k$-anonymity
type disclosure guarantee for each point in the dataset. Previous algorithms
for univariate microaggregation had a $O(k n)$ time complexity. By rephrasing
microaggregation as an instance of the concave least weight subsequence
problem, in this work we provide improved algorithms that provide an optimal
univariate microaggregation on sorted data in $O(n)$ time and space. We further
show that our algorithms work not only for sum of squares cost functions, as
typically considered, but seamlessly extend to many other cost functions used
for univariate microaggregation tasks. In experiments we show that the
presented algorithms lead to real world performance improvements.
