Abstract
BackgroundModern data-driven approaches to medical research require patient-level information at comprehensive depth and breadth. To create the required big datasets, information from disparate sources can be integrated into clinical and translational warehouses. This is typically implemented with Extract, Transform, Load (ETL) processes, which access, harmonize and upload data into the analytics platform. ObjectivePrivacy-protection needs careful consideration when data is pooled or re-used for secondary purposes, and data anonymization is an important protection mechanism. However, common ETL environments do not support anonymization, and common anonymization tools cannot easily be integrated into ETL workflows. The objective of the work described in this article was to bridge this gap. MethodsOur main design goals were (1) to base the anonymization process on expert-level risk assessment methodologies, (2) to use transformation methods which preserve both the truthfulness of data and its schematic properties (e.g. data types), (3) to implement a method which is easy to understand and intuitive to configure, and (4) to provide high scalability. ResultsWe designed a novel and efficient anonymization process and implemented a plugin for the Pentaho Data Integration (PDI) platform, which enables integrating data anonymization and re-identification risk analyses directly into ETL workflows. By combining different instances into a single ETL process, data can be protected from multiple threats. The plugin supports very large datasets by leveraging the streaming-based processing model of the underlying platform. We present results of an extensive experimental evaluation and discuss successful applications. ConclusionsOur work shows that expert-level anonymization methodologies can be integrated into ETL workflows. Our implementation is available under a non-restrictive open source license and it overcomes several limitations of other data anonymization tools.
Highlights
Modern medical research requires data of comprehensive depth and breadth to improve our understanding of the development and course of diseases and to develop methods for prevention, targeted diagnosis and therapy
Comparison with prior work: We first compared the performance of our plugin to sdcMicro [18], which features a cell suppression algorithm that has been implemented in C++ and linked into the software
We studied the utility of output data produced by our cell suppression method in comparison to other data transformation methods using the concept of privacy-preserving data cubes proposed by Kim et al [39]
Summary
Modern medical research requires data of comprehensive depth and breadth to improve our understanding of the development and course of diseases and to develop methods for prevention, targeted diagnosis and therapy. In a learning health system “every clinical encounter contributes to research and research is being applied in real time to clinical care” [1] To implement this on a large scale, data must be made accessible, harmonized and integrated [2,3]. To create the required big datasets, information from disparate sources can be integrated into clinical and translational warehouses. This is typically implemented with Extract, Transform, Load (ETL) processes, which access, harmonize and upload data into the analytics platform. Results: We designed a novel and efficient anonymization process and implemented a plugin for the Pentaho Data Integration (PDI) platform, which enables integrating data anonymization and re-identification risk analyses directly into ETL workflows. Our implementation is available under a non-restrictive open source license and it overcomes several limitations of other data anonymization tools
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