Algorithm 1 Clustering-Based Anonymizer (CBA)
	input: Dataset , utility policy , comprised of potentially identifying sets of codes, and k
	output: Anonymized dataset
1.	←
2.	Populate a priority queue PQ with all sets of codes in
3.	while (PQ is not empty)
4.	Retrieve the top-most set of codes p from PQ
5.	foreach (im ∈ p)
6.	if (im is a generalized term)
7.	im ← the set of ICD codes mapped to im
8.	if (sup(p, ) ≥ k)
9.	remove p from PQ
10.	else
11.	while (sup(p, ) < k)
12.	find a pair {im, is} such that im is contained in p,
	im and is are contained in the same utility
	constraint u ∈ and ILM( (im, is) ) is minimal
13.	ĩ ← anonymize({im, is}, p)
14.	update p by replacing {im,is} with ĩ
15.	store the mapping of ĩ with the set of all ICD codes
	contained in it in {im,is}
16.	remove p from PQ
17.	return