A Novel Geographic Partitioning System for Anonymizing Health Care Data

TL;DR

This paper introduces a new geographic-based system for anonymizing healthcare data, balancing privacy protection with geographic information preservation, and compares different approaches for optimal data anonymization.

Contribution

The paper presents a novel geographic partitioning system for health data anonymization and evaluates various approaches to optimize privacy and data utility.

Findings

Different approaches have varying effectiveness in balancing privacy and geographic detail.

The proposed system can be tailored to specific privacy and utility requirements.

Recommendations are provided for selecting the best approach based on user needs.

Abstract

With large volumes of detailed health care data being collected, there is a high demand for the release of this data for research purposes. Hospitals and organizations are faced with conflicting interests of releasing this data and protecting the confidentiality of the individuals to whom the data pertains. Similarly, there is a conflict in the need to release precise geographic information for certain research applications and the requirement to censor or generalize the same information for the sake of confidentiality. Ultimately the challenge is to anonymize data in order to comply with government privacy policies while reducing the loss in geographic information as much as possible. In this paper, we present a novel geographic-based system for the anonymization of health care data. This system is broken up into major components for which different approaches may be supplied. We compare such approaches in order to make recommendations on which of them to select to best match user requirements.