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Privacy evaluation and secure sharing of synthetic tabular data using FASA framework

human-computer-interaction
computational-science
data-mining-and-machine-learning

Abstract

In medical care and other fields, the exchange of information is essential for innovation. However, data sharing is often limited by policies designed to protect individual privacy. While generated information in tables can effectively facilitate data sharing, it does not guarantee security. Currently, there is a lack of clarity on how to evaluate the secure features of simulated data, complicating the analysis of findings among researchers. This initial study has identified standardized security assessment indicators for simulated data presented in tables. Usable requirements enable research comparisons and help non-technical users understand privacy protections. Our evaluation criteria, Fairness, Auditability, Scalability, and Assurance (FASA), form the basis of our methodology. We employ a scoring system from 1 to 4 to gauge how well the evaluation requirements align with the FASA concepts. Each element is scored across four factors, resulting in 16 dimensions. The assessment of FASA concepts and criteria draws on established measures from previous research to evaluate their significance and effectiveness. The findings assess strategies and highlight opportunities for improvement by outlining their strengths and weaknesses. These FASA standards could assist researchers and institutions in establishing consistent security assessment criteria for both synthetic and real tabular data.
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