In this paper, the authors propose a model for privacy location sharing, and investigate the relationships between the sharing behaviors and location characteristics and tracking methods. During the modeling process, it is meaningful to introduce entropy and apply it in later statistical analysis. However, I think that the other settings may lead to bias when conclude the results. The most influential factors may be the investigating system. How to communicate with participants and what they know about this project can lead to different location sharing behaviors. In my perspectives, the best datasets for analyzing privacy in location sharing are produced by users in daily life. The empirical environment is not natural for users and likely to change their behaviors. For example, we may doubt whether participants are more willing to share locations because they get pay from this project. We also don’t know whether they share locations on purpose in this project, which is not the natural states. Therefore, limitations can include that data come from particular applications and devices provided by researchers.
It is tricky to measure how comfortable people are willing to share their locations. Hence, it is necessary to ensure the natural inputs from participants (i.e., lessen the systematic errors). There are two possible ways we can improve privacy analysis in location sharing. In one hand, we should collect datasets from people’s daily life, which ensure the randomness of data. In the other hand, we can have more comprehensive data, participants, and context to simulate the natural environment rather than rely on a simple model.