Estimation of compensation scheme functions according to the level of image data de-identification*

With the rapid expansion of data markets, accurately assessing the value of data has become critical to fostering trust and fairness in data transactions. However, the lack of standardized methods for evaluating the worth of personal information, particularly personal image data, poses a significant challenge. This study examines how different levels of de-identification impact the perceived value of image data. Using the Structural Similarity Index Measure (SSIM), we assess changes in image quality, and through regression analysis, we observe a marked decrease in data value as de-identification levels increase. Our findings emphasize the importance of developing compensation models that align privacy preservation with data valuation, contributing to the creation of a transparent and secure data trading environment.