Individual fairness testing evaluates a machine learning (ML) classifier by estimating its individual fairness ratio (IFr) and has become an important approach for assessing ML classifiers. However, existing studies on ML fairness testing focus exclusively on the Fairness Through Awareness (FTA) setting, where protected attributes are explicitly used. In this study, we introduce the first framework for individual fairness testing under Fairness Through Unawareness (FTU), where protected attributes are not explicitly available. The proposed framework, called IFT_UNAWARE , evaluates a classifier by estimating its IFr in the FTU setting. It extends individual fairness testing approaches developed for FTA by incorporating techniques, including protected attribute inference with confidence control, a generalized notion of individual similarity, and statistically guaranteed termination criteria. Our experiments show that IFT_UNAWARE can compute IFr estimates with statistical guarantees and demonstrate the effectiveness of both the generalized simi- larity notion and confidence-based protected attribute inference. These results highlight both the feasibility and the limitations of assessing individual fairness under FTU and provide a foundation for future methodological advances.