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Interested in the below or volunteering for GenV in a different capacity? Reach out to GenV’s Student Coordinator Ella Perlow. 

A comparative analysis of hashing algorithms for the generation of research study participant identifiers

A comparative analysis of hashing algorithms for the generation of research study participant identifiers

Description:

Generation Victoria is Australia’s most ambitious children’s study, aiming to recruit all 160,000 Victorian babies born over 2 full years from October 2021 and their parents. With guiding principles of Inclusion and Equity, GenV is designed to allow every eligible Victorian to participate (eg materials available in >25 languages, very low burden digital assessments).

The provision of data for both research and linkage of datasets is a fundamental part of the GenV study being successful. To protect the privacy of study participants it is necessary to be able to de-identify data but still provide an identifier that can be referenced in data sets for research or for data linkage. There are many algorithms that can be used to do so which have differing profiles in terms of being able to generate a unique identifier, being resilient to cybersecurity attacks, and the computational intensity required to generate the ID. The profile of the algorithms will determine the suitability of the algorithm to be used in the GenV platform.

This analysis will enable GenV to decide on the most appropriate algorithm to adopt to provide external identifiers.

The task is to research possible algorithms, implement them into code, perform benchmark analysis, and write up the results in a whitepaper containing and problem definition, analysis that has been carried out, and comparative results.

Supervisor: Simon Hall