Leveraging Differential Privacy in Geospatial Analyses of Standardized Healthcare Data

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

We present a collection of geodatabase functions which expedite utilizing differential privacy for privacy-aware geospatial analysis of healthcare data. The healthcare domain has a long history of standardization and research communities have developed open-source common data models to support the larger goals of interoperability, reproducibility, and data sharing; these models also standardize geospatial patient data. However, patient privacy laws and institutional regulations complicate geospatial analyses and dissemination of research findings due to protective restrictions in how data and results are shared. This results in infrastructures with great abilities to organize and store healthcare data, yet which lack the innate ability to produce shareable results that preserve privacy and conform to regulatory requirements. Differential privacy is a model for performing privacy-preserving analytics. We detail our process and findings in inserting an open-source library for differential privacy into a workflow for leveraging a geodatabase for geocoding and analyzing geospatial data stored as part of the Observational Medical Outcomes Partnership (OMOP) common data model. We pilot this process using an open big data repository of addresses.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
EditorsXintao Wu, Chris Jermaine, Li Xiong, Xiaohua Tony Hu, Olivera Kotevska, Siyuan Lu, Weijia Xu, Srinivas Aluru, Chengxiang Zhai, Eyhab Al-Masri, Zhiyuan Chen, Jeff Saltz
Pages3119-3122
Number of pages4
ISBN (Electronic)9781728162515
DOIs
StatePublished - Dec 10 2020
Event8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States
Duration: Dec 10 2020Dec 13 2020

Publication series

NameProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

Conference

Conference8th IEEE International Conference on Big Data, Big Data 2020
Country/TerritoryUnited States
CityVirtual, Atlanta
Period12/10/2012/13/20

Bibliographical note

Funding Information:
The project described was supported by the NIH National Center for Advancing Translational Sciences through grant number UL1TR001998.

Publisher Copyright:
© 2020 IEEE.

Keywords

  • big data applications
  • data privacy
  • geographic information systems

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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