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

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

4 Citations (Scopus)


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 publicationCD-ARES 2016: Availability, Reliability, and Security in Information Systems
Number of pages11
Place of PublicationCham
Publication date23.08.2016
ISBN (Print)978-3-319-45506-8
ISBN (Electronic)978-3-319-45507-5
Publication statusPublished - 23.08.2016
MoE publication typeA4 Article in conference proceedings

Publication series

Name Lecture Notes in Computer Science book series (LNCS)


  • 512 Business and Management
  • Distance Function
  • Vector Quantization
  • Pairwise Distance
  • Differential Privacy
  • Cluster Element


Dive into the research topics of 'Data Anonymization as a Vector Quantization Problem: Control Over Privacy for Health Data'. Together they form a unique fingerprint.

Cite this