Programmatically encrypting data linkage fields at a project level within the Secure Anonymised Information Linkage (SAIL) databank IJPDS (2017) Issue 1, Vol 1:151, Proceedings of the IPDLN Conference (August 2016)

Main Article Content

Richard Noyce
Dan Thayer

Abstract

ABSTRACT


Introduction/Background
The ability to link datasets within the Secure Anonymised Information Linkage (SAIL) databank provides researchers with a powerful tool to analyse multiple datasets. The ability to combine several datasets also has the adverse effect of potential identification of an individual. Further encrypting linkage fields at a project level limits the links to datasets specific to the project only. This presentation discusses the opensource web based administration tool that programmatically applies project encryption in a consistent and timely manner, logging administrator actions.


Objectives
1). Identify encryption methodology
2). Programme encryption steps and log steps
3). Design and implement web based user administration tool


Approach
Utilising existing Secure Anonymised Information Linkage (SAIL) databank security, providing researchers with a view of their data, separating data linkage fields into a separate secure lookup table. Using Python programming language to automate the Structured Query Language (SQL) scripts required to accomplish this, as well as Python packages to interact with the databank and web based administration tool.


Results
Project encrypted views created for several projects and scores of datasets. Encrypted linkage fields unique to each project ensuring views across projects can not be linked either to each other or the original datasets.


Conclusion
Encryption process is programmable and administered through web tool.

Article Details

How to Cite
Noyce, R. and Thayer, D. (2017) “Programmatically encrypting data linkage fields at a project level within the Secure Anonymised Information Linkage (SAIL) databank: IJPDS (2017) Issue 1, Vol 1:151, Proceedings of the IPDLN Conference (August 2016)”, International Journal of Population Data Science, 1(1). doi: 10.23889/ijpds.v1i1.170.