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An Algorithm for K-Anonymous Microaggregation and Clustering Inspired by the Design of Distortion-Optimized Quantizers

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Executive Summary

The authors present a multidisciplinary solution to the problems of anonymous micro-aggregation and clustering, illustrated with two applications, namely privacy protection in databases, and private retrieval of location-based information. Their solution is perturbative, is based on the same privacy criterion used in microdata k-anonymization, and provides anonymity through a substantial modification of the Lloyd algorithm, a celebrated quantization design algorithm, endowed with numerical optimization techniques. Their algorithm is particularly suited to the important problem of k-anonymous micro-aggregation of databases, with a small integer k representing the number of individual respondents indistinguishable from each other in the published database.

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