  For the safe sharing pre-trained language models, no guidelines exist at
present owing to the difficulty in estimating the upper bound of the risk of
privacy leakage. One problem is that previous studies have assessed the risk
for different real-world privacy leakage scenarios and attack methods, which
reduces the portability of the findings. To tackle this problem, we represent
complex real-world privacy leakage scenarios under a universal
parameterization, \textit{Knowledge, Anonymization, Resource, and Target}
(KART). KART parameterization has two merits: (i) it clarifies the definition
of privacy leakage in each experiment and (ii) it improves the comparability of
the findings of risk assessments. We show that previous studies can be simply
reviewed by parameterizing the scenarios with KART. We also demonstrate privacy
risk assessments in different scenarios under the same attack method, which
suggests that KART helps approximate the upper bound of risk under a specific
attack or scenario. We believe that KART helps integrate past and future
findings on privacy risk and will contribute to a standard for sharing language
models.
