title: Anonymisation of geographical distance matrices via Lipschitz embedding.
abstract: BACKGROUND
  Anonymisation of spatially referenced data has received increasing attention in recent years. Whereas the research focus has been on the anonymisation of point locations, the disclosure risk arising from the publishing of inter-point distances and corresponding anonymisation methods have not been studied systematically.
  
  
  METHODS
  We propose a new anonymisation method for the release of geographical distances between records of a microdata file--for example patients in a medical database. We discuss a data release scheme in which microdata without coordinates and an additional distance matrix between the corresponding rows of the microdata set are released. In contrast to most other approaches this method preserves small distances better than larger distances. The distances are modified by a variant of Lipschitz embedding.
  
  
  RESULTS
  The effects of the embedding parameters on the risk of data disclosure are evaluated by linkage experiments using simulated data. The results indicate small disclosure risks for appropriate embedding parameters.
  
  
  CONCLUSIONS
  The proposed method is useful if published distance information might be misused for the re-identification of records. The method can be used for publishing scientific-use-files and as an additional tool for record-linkage studies.
authors.affiliation: Research Methodology Group, University of Duisburg-Essen, Lotharstraße 65, 47057, Duisburg, Germany. martin.kroll@uni-due.de.
authors.forename: Martin
authors.initials: M
authors.lastname: Kroll
authors.affiliation: Research Methodology Group, University of Duisburg-Essen, Lotharstraße 65, 47057, Duisburg, Germany. rainer.schnell@city.ac.uk.|City University London, Northampton Square, EC1V 0HB, London, UK. rainer.schnell@city.ac.uk.
authors.forename: Rainer
authors.initials: R
authors.lastname: Schnell
mesh_terms: D000068240:Data Anonymization / Q000592:standards*; D016208:Databases, Factual / Q000592:standards* / Q000706:statistics & numerical data; D006801:Humans; D000068241:Personally Identifiable Information / Q000706:statistics & numerical data / Q000639:trends*; D056510:Position-Specific Scoring Matrices