Abstract

A method of fixed cardinality partition is examined. This methodology can be applied on many problems, such as the confidentiality protection, in which the protection of confidential information has to be ensured, while preserving the information content of the data. The basic feature of the technique is to aggregate the data into m groups of small fixed size k, by minimizing Bregman divergences. It is shown that, in the case of non-uniform probability measures the groups of the optimal solution are not necessarily separated by hyperplanes, while with uniform they are. After the creation of an initial partition on a real data-set, an algorithm, based on two different Bregman divergences, is proposed and applied. This methodology provides us with a very fast and efficient tool to construct a near-optimum partition for the (m×k)-partitioning problem.

Keywords: Confidentiality; Data masking; Fixed cardinality partitioning; Fixed size micro-aggregation; Bregman divergences; Pythagorean property; Convex partition

Article Outline

1. Introduction

2. Micro-aggregation and formalization of the general problem

3. The Bregman divergences in the (m×k)-partitioning problem

4. The (k,n-k)-partitioning problem

5. The optimal (m×k) partition

6. Experimental results

7. Conclusions

Acknowledgements

References