Title: Individual Fairness of Data Provider Regarding Privacy Risk and Gain. Abstract: Fairness and privacy risks are important concerns of machine learning (ML) when deploying ML to the real world. Recent studies have focused on group fairness and privacy protection, but no study focuses on individual fairness (IF) and privacy protection. In this paper, we propose a new definition of IF from the perspective of privacy protection and experimentally evaluate privacy-preserving ML based on the proposed IF. For the proposed definition, we assume that users provide their data to an ML service and consider the principle that all users should obtain gains corresponding to their privacy risks. As a user's gain, we calculate the accuracy improvement on the user's data when providing the data to the ML service. We conducted experiments on the image and tabular datasets using three neural networks (NNs) and two tree-based algorithms with differential privacy guarantee. The experimental results of NNs show that we cannot stably improve the proposed IF by changing the strength of privacy protection and applying defenses against membership inference attacks. The results of tree-based algorithms show that privacy risks were extremely small without depending on the strength of privacy protection but raise a new question about the motivation of users for providing their data.