The protection of sensitive physiological data, such as pupil size measurements, is becoming increasingly critical both from a research perspective and due to ever-stricter regulatory requirements. Nevertheless, the impact of anonymization procedures on the scientific utility of such data remains relatively underexplored and often insufficiently understood.
This study systematically examines how different levels of anonymization, applied via quantization, aggregation and noise addition, impact the ability to distinguish between user interfaces in user experience evaluation. Using time series data of pupil size and electrodermal measurements collected from two user interfaces, we quantify the trade-off between privacy and data utility.
The results show that higher levels of anonymization reduce effect sizes and the statistical significance of differences, but limit the ability to detect meaningful patterns. Furthermore, the study emphasizes that each anonymization procedure requires a specific approach to assessing the appropriate anonymization level.
These findings highlight the necessity of carefully balancing personal data protection with the retention of analytical value. At the same time, they provide a practical framework for selecting an optimal anonymization level that ensures legal compliance while maintaining the usability of data in research and applied settings.
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