Geographically Masking Addresses to Study COVID-19 Clusters

Abstract

The spatial analysis of health data usually raises geoprivacy issues. But with the virulence of COVID-19, scientists and crisis managers do need to analyse the spatio-temporal distribution and spreading of the disease with spatially precise data. In particular, it is useful to locate each case on a map to identify clusters of cases in space and time. To allow such analyses with breach of geoprivacy, geomasking techniques are necessary. This paper experiments the geomasking techniques from the literature to solve this problem: masking the real address of positive cases while preserving the local 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 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.

A Comprehensive Analysis of Privacy Protection Techniques Developed for COVID-19 Pandemic

“…T HE ongoing coronavirus disease-2019 (COVID- 19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is one of the most extraordinary challenges humanity has ever faced in terms of infectious diseases. During this pandemic, an enormous number of people got infected, and millions lost their lives.…”

“…Thus far, a considerable effort has been made to address privacy concerns stemming from COVID-19-related digital solutions and the data collection/processing to curtail the pandemic. Noticeable and remarkable development/research efforts include the decentralized and privacy-ensured contact tracing systems of Apple and Google [9], privacy-assured contact tracing based on call data record analysis (CDRA) [10], privacy-preserved tracking of suspected COVID-19 infections [11], personal data-protection laws [12], informed processing of personal data [13], consent-based data utilization [14], statistical disclosure control (SDC) techniques [15], data sharing based on the recommendations of the RDA [16], responsible data governance [17], blockchain-based privacy preserving systems [18], anonymized data based cluster identification [19], artificial intelligence-driven software for privacy protection [20], and differential privacy-based privacy protection methods [21], to name a few. Although these methods have contributed firmly to addressing different privacy requirements, cohesive and substantial efforts are still needed from the research and development community to curtail privacy breaches and personal-data abuses in the ongoing/post-COVID-era.…”