True spatial k-anonymity: adaptive areal elimination vs. adaptive areal masking

Research output: Contribution to journalArticlepeer-review

Abstract

Spatial anonymization of address points is critical to fields such as public health. There have been recent concerns about applications of geomasks that did not guarantee the level of k-anonymity theoretically expected. An analysis of the problem and a potential solution were previously proposed: Adaptive Areal Elimination (AAE). The present paper expands on AAE and proposes a modified version, Adaptive Areal Masking (AAM). A benchmark comparison of both methods is conducted, which shows that AAM outperforms AAE in most configurations tested. The discussion attempts to identify the application cases for which AAE might still be preferable and addresses documentation needs with both methods.

Original languageEnglish (US)
Pages (from-to)537-549
Number of pages13
JournalCartography and Geographic Information Science
Volume47
Issue number6
DOIs
StatePublished - Nov 1 2020

Bibliographical note

Funding Information:
The work for this paper was supported by a Pilot Project Grant through the Midwest Center for Occupational Health and Safety (MCOHS) Education and Research Center, University of Minnesota (UMN), Subaward NIOSH T42OH008434.

Keywords

  • Spatial anonymization
  • geomasking
  • k-anonymity
  • privacy
  • spatial aggregation

Fingerprint Dive into the research topics of 'True spatial k-anonymity: adaptive areal elimination vs. adaptive areal masking'. Together they form a unique fingerprint.

Cite this