Faculty, Staff and Student Publications
Language
English
Publication Date
11-1-2023
Journal
Proceedings of the ACM International Conference on Advances in Geographic Information Systems
DOI
10.1145/3589132.3625656
PMID
38152352
PMCID
PMC10751042
PubMedCentral® Posted Date
12-22-2024
PubMedCentral® Full Text Version
Author MSS
Abstract
Measuring spatial accessibility to healthcare resources and facilities has long been an important problem in public health. For example, during disease outbreaks, sharing spatial accessibility data such as individual travel distances to health facilities is vital to policy making and designing effective interventions. However, sharing these data may raise privacy concerns, as information about individual data contributors (e.g., health status and residential address) may be disclosed. In this work, we investigate those unintended information leakage in spatial accessibility analysis. Specifically, we are interested in understanding whether sharing data for spatial accessibility computations may disclose individual participation (i.e., membership inference) and personal identifiable information (i.e., address inference). Furthermore, we propose two provably private algorithms that mitigate those privacy risks. The evaluation is conducted with real population and healthcare facilities data from Mecklenburg county, NC and Nashville, TN. Compared to state-of-the-art privacy practices, our methods effectively reduce the risks of membership and address disclosure, while providing useful data for spatial accessibility analysis.
Keywords
Privacy, Spatial Accessibility, Health Informatics
Published Open-Access
yes
Recommended Citation
Liyue Fan and Luca Bonomi, "Hide Your Distance: Privacy Risks and Protection in Spatial Accessibility Analysis" (2023). Faculty, Staff and Student Publications. 893.
https://digitalcommons.library.tmc.edu/uthshis_docs/893