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Anonymisation of geographical distance matrices via Lipschitz embedding
Martin Kroll1, Rainer Schnell2,3
1Research Methodology Group, University of Duisburg-Essen, Lotharstraße 65, 47057, Duisburg, Germany. martin.kroll@uni-due.de.
International Journal of Health Geographics
|January 8, 2016
Summary
This study introduces a novel anonymization technique for geographical distance data, significantly reducing disclosure risks. The method effectively protects sensitive information in microdata releases, enhancing data privacy.
Area of Science:
- Spatial data anonymization
- Data privacy and security
- Geospatial information systems
Background:
- Growing attention on anonymizing spatially referenced data.
- Limited research on disclosure risks from publishing inter-point distances.
- Need for systematic study of anonymization methods for distance data.
Purpose of the Study:
- Propose a new anonymization method for geographical distances in microdata.
- Address the systematic study of anonymization for inter-point distances.
- Evaluate the disclosure risk associated with publishing distance matrices.
Main Methods:
- Developed a novel anonymization technique using a variant of Lipschitz embedding.
- Proposed a data release scheme including microdata and a distance matrix.
- Modified distances to better preserve smaller values than larger ones.
Main Results:
- Evaluated anonymization effectiveness through linkage experiments with simulated data.
- Demonstrated small disclosure risks with appropriate embedding parameters.
- Quantified the impact of embedding parameters on data disclosure risk.
Conclusions:
- The proposed method is valuable for preventing misuse of published distance information for re-identification.
- Applicable for creating secure scientific-use files from sensitive microdata.
- Serves as an additional tool for enhancing record-linkage studies.
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