ANovel Local SearchMethod forMicroaggregation

In this paper, we propose an effective microaggregation algorithm to produce a more useful protected data for publishing. Microaggregation is mapped to a clustering problem with known minimum and maximum group size constraints. In this scheme, the goal is to cluster n records into groups of at least k and at most 2k−1 records, such that the sum of the within-group squared error (SSE) is minimized. We propose a local search algorithm which iteratively satisfies the constraints of the optimal solution of the problem. The algorithm solves the problem in O(n) time. Experimental results on real and synthetic data sets with different distributions demonstrate the effectiveness of the method in producing useful protected data sets. © 2015 ISC. All rights reserved.