Data anonymization as a vector quantization problem: Control over privacy for health data

Yoan Miche*, Ian Oliver, Silke Holtmanns, Aapo Kalliola, Anton Akusok, Amaury Lendasse, Kaj Mikael Björk

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

4 Citations (Scopus)

Abstract

This paper tackles the topic of data anonymization from a vector quantization point of view. The admitted goal in this work is to provide means of performing data anonymization to avoid single individual or group re-identification from a data set, while maintaining as much as possible (and in a very specific sense) data integrity and structure. The structure of the data is first captured by clustering (with a vector quantization approach), and we propose to use the properties of this vector quantization to anonymize the data. Under some assumptions over possible computations to be performed on the data, we give a framework for identifying and “pushing back outliers in the crowd”, in this clustering sense, as well as anonymizing cluster members while preserving cluster-level statistics and structure as defined by the assumptions (density, pairwise distances, cluster shape and members…).

Original languageEnglish
Title of host publicationAvailability, Reliability, and Security in Information Systems - IFIP WG 8.4, 8.9, TC 5 International Cross-Domain Conference, CD-ARES 2016 and Workshop on Privacy Aware Machine Learning for Health Data Science, PAML 2016 Salzburg, Proceedings
EditorsFrancesco Buccafurri, Andreas Holzinger, Peter Kieseberg, A. Min Tjoa, Edgar Weippl
PublisherSpringer Verlag
Pages193-203
Number of pages11
Volume9817
ISBN (Electronic)978-3-319-45507-5
ISBN (Print)9783319455068
DOIs
Publication statusPublished - 2016
MoE publication typeA4 Article in a conference publication
EventIFIP WG 8.4, 8.9, TC 5 International Cross-Domain Conference, CD-ARES 2016 and Workshop on Privacy Aware Machine Learning for Health Data Science, PAML 2016 - Salzburg, Austria
Duration: 31 Aug 20162 Sep 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9817
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

ConferenceIFIP WG 8.4, 8.9, TC 5 International Cross-Domain Conference, CD-ARES 2016 and Workshop on Privacy Aware Machine Learning for Health Data Science, PAML 2016
CountryAustria
CitySalzburg
Period31/08/201602/09/2016

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