Grouping of variables to facilitate SDL methods in multivariate data sets

Anna Oganian, Ionut Iacob, Goran Lesaja

Research output: Contribution to book or proceedingConference articlepeer-review

3 Scopus citations

Abstract

Data sets that are subject to Statistical Disclosure Limitation (SDL) often have many variables of different types that need to be altered for disclosure limitation. To produce a good quality public data set, the data protector needs to account for the relationships between the variables. Hence, ideally SDL methods should not be univariate, that is, treating each variable independently of others, but multivariate, handling many variables at the same time. However, if a data set has many variables, as most government survey data do, the task of developing and implementing a multivariate approach for SDL becomes difficult. In this paper we propose a pre-masking data processing procedure which consists of clustering the variables of high dimensional data sets, so that different groups of variables can be masked independently, thus reducing the complexity of SDL. We consider different hierarchical clustering methods, including our version of hierarchical clustering algorithm, that we call K-Link, and outline how the data protector can define an appropriate number of clusters for these methods. We implemented and applied these methods to two genuine multivariate data sets. The results of the experiments show that K-Link has a potential to solve this problem efficiently. The success of the method, however, depends on the correlation structure of the data. For the data sets where most of the variables are correlated, clustering of variables and subsequent independent application of SDL methods to different clusters may lead to attenuated correlation in the masked data, even for efficient clustering methods. Thereby, the proposed approach is a trade-off between the computational complexity of multivariate SDL methods and data utility loss due to independent treatment of different clusters by SDL methods.

Original languageEnglish
Title of host publicationPrivacy in Statistical Databases - UNESCO Chair in Data Privacy, International Conference, PSD 2018, Proceedings
EditorsFrancisco Montes, Josep Domingo-Ferrer
PublisherSpringer Verlag
Pages187-199
Number of pages13
ISBN (Print)9783319997704
DOIs
StatePublished - 2018
EventInternational Conference on Privacy in Statistical Databases, PSD 2018 - Valencia, Spain
Duration: Sep 26 2018Sep 28 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11126 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Privacy in Statistical Databases, PSD 2018
Country/TerritorySpain
CityValencia
Period09/26/1809/28/18

Scopus Subject Areas

  • Theoretical Computer Science
  • General Computer Science

Keywords

  • Dimensionality reduction
  • Hierarchical clustering
  • Statistical Disclosure Limitation (SDL)

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