A Linear-Time Multivariate Micro-aggregation for Privacy Protection in Uniform Very Large Data Sets

Authors:
Agusti Solanas;Roberto Pietro
Affiliations:
UNESCO Chair in Data Privacy Dept. Computer Engineering and Mathematics, Rovira i Virgili University, Tarragona E-43001;UNESCO Chair in Data Privacy Dept. Computer Engineering and Mathematics, Rovira i Virgili University, Tarragona E-43001
Venue:
MDAI '08 Sabadell Proceedings of the 5th International Conference on Modeling Decisions for Artificial Intelligence
Year:
2008

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Abstract

Optimally micro-aggregating a multivariate data set is known to be NP-hard, thus, heuristic approaches are used to cope with this privacy preserving problem. Unfortunately, algorithms in the literature are computationally costly, and this prevents using them on large data sets.We propose a partitioning algorithm to micro-aggregate uniform very large data sets with cost O(n). We provide the mathematical foundations proving the efficiency of our algorithm and we show that the error associated to micro-aggregation is bounded and decreases when the number of micro-aggregated records grows. The experimental results confirm the prediction of the mathematical analysis. In addition, we provide a comparison between our proposal and MDAV, a well-known micro-aggregation algorithm with cost O(n2).