CANDIDATE: A tool for generating anonymous participant-linking IDs in multi-session studies

Area of Science:

• Computer Science
• Research Methodology
• Data Privacy

Background:

• Participant privacy is an ethical and legal requirement in research.
• Linking longitudinal data and expanding participant samples pose technical challenges for anonymity.
• Ensuring participant anonymity is crucial for ethical research practices.

Purpose of the Study:

• To introduce the CANDIDATE tool for generating simple, unique, and anonymous participant IDs.
• To simplify the administration of studies requiring participant tracking over time.
• To address the technical difficulties in maintaining participant anonymity.

Main Methods:

• Simulations were employed to validate the uniqueness of generated IDs.
• Anonymity of the generated IDs was assessed through simulation.
• The study evaluated the trade-off between data integrity and anonymity.

Main Results:

• The CANDIDATE tool successfully generates IDs with a low collision rate.
• High levels of participant anonymity are maintained by the tool.
• An optimal balance between data integrity and anonymity is achieved when the ID space is approximately ten times the number of participants.

Conclusions:

• The CANDIDATE tool facilitates the collection of comprehensive longitudinal empirical evidence.
• The tool provides a more robust foundation for drawing reliable conclusions from research data.
• An open-source, browser-based implementation of the CANDIDATE tool is available for researchers.