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For the safe sharing pre-trained language models, no guidelines exist atpresent owing to the difficulty in estimating the upper bound of the risk ofprivacy leakage. One problem is that previous studies have assessed the riskfor different real-world privacy leakage scenarios and attack methods, whichreduces the portability of the findings. To tackle this problem, we representcomplex real-world privacy leakage scenarios under a universalparameterization, \textit{Knowledge, Anonymization, Resource, and Target}(KART). KART parameterization has two merits: (i) it clarifies the definitionof privacy leakage in each experiment and (ii) it improves the comparability ofthe findings of risk assessments. We show that previous studies can be simplyreviewed by parameterizing the scenarios with KART. We also demonstrate privacyrisk assessments in different scenarios under the same attack method, whichsuggests that KART helps approximate the upper bound of risk under a specificattack or scenario. We believe that KART helps integrate past and futurefindings on privacy risk and will contribute to a standard for sharing languagemodels.

KART: Parameterization of Privacy Leakage Scenarios from Pre-trained Language Models