Abstract
ABSTRACT The spatial analysis of health data usually raises geoprivacy issues. Due to the virulence of COVID-19, scientists and crisis managers do need to analyze the distribution and spread of the disease with spatially precise data. In particular, it is useful to locate each case on a map to identify clusters of cases. To allow such analyses without breach of geoprivacy, geomasking techniques are necessary. This paper experiments with the geomasking techniques from the literature to solve this problem: masking the real address of positive cases while preserving the local spatial cluster patterns. In particular, two different approaches based on aggregation and perturbation are adapted to the geomasking of addresses in areas with different densities of population. A new simulated cluster crowding method is also proposed to preserve clusters as much as possible. The results show that geomasking techniques can spatially anonymize addresses while preserving clusters, and the best geomasking method depends on the use of the anonymized data.
Highlights
The spatio-temporal analysis of cases is a good way an epidemic, and the recent COVID-19 pandemic generated a huge amount of data
Analysing this raw data, with for instance the address of the people who contracted COVID-19, raises some privacy issues, and geomasking is necessary to preserve both people privacy and the spatial accuracy required for analysis
This paper proposes di erent geomasking techniques adapted to this COVID-19 data
Summary
The spatio-temporal analysis of cases is a good way an epidemic, and the recent COVID-19 pandemic generated a huge amount of data. Walid Houfaf-Khoufaf Universite Gustave Eiffel, ENSG, IGN ) Institut national de l’information géographique et forestière https://orcid.org/0000-0001-6113-6903
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