Geographic Partitioning Techniques for the Anonymization of Health Care Data

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. trade-off between the level of protection that can be achieved on a data set and the resultant utility of the data [13,14,17,34]. Although it is desirable to minimize the loss of any type of information in the data set, in some cases the preservation of geographic information may be of particular interest. Studies which involve the propagation of diseases across geographic areas require a high level of precision in the geographic information of the data set [23]. Any form of location-critical research such as spatial epidemiology requires high precision geographic information in order to be carried out [5,28,36]. However, the release of these precise geographic details greatly increases the risk of disclosure of confidential information due to higher levels of distinctness in the records of the data set. This risk creates a barrier in the disclosure of essential geographic information.In this paper, we present a novel and configurable system to achieve k-anonymity [34,35] on a data set through the use of geographic partitioning guided by the use of Voronoi diagrams [22]. This system, namedVoronoi-Based Aggregation System (VBAS), achieves anonymity in a data set through the generalization (coarsening of the level of precision) of geographic attributes and the suppression of records. By aggregating regions, we avoid the need for the suppression of small regions, which can lead to heavily censored data sets [10,20], while maintaining a higher degree of geographic precision than other methods (such as cropping [7,21]).Since any loss in geographic information has negative effects on the ability to effectively analyze a data set, we postulate it is desirable to preserve as much geographic information as possible [27]. VBAS addresses this problem by aggregating small regions of fine granularity into larger regions that satisfy criteria for achieving a sufficient level of anonymity while reducing the loss of geographic information. In order to evaluate the quality of the resultant aggregation, we employ measures of suppression an...

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“…In the data anonymization, the information loss measures how well the masked dataset and the generalized attributes approximate the original ones. The information loss imposed by the masking dataset D toD in an anonymization process can be measured by using the non-uniform entropy (NE) as follows [45,47]:…”

“…This metric is based on the probability of correctly guessing the original attribute of a record given its generalized data. The calculation of Pr is as follows [47]:…”

“…To summarize, greater privacy is commensurate with increased information loss and a decrease in the data utility level. A balance between the level of privacy and the information loss is needed to achieve an appropriate level of data security with the resultant utility of the data (e.g., data analysis) [47].…”