 When considering fairness in event logs or process mining, sensitive attributes are those that could potentially lead to discrimination or bias in the analysis or automated decision-making based on the process. Here are the sensitive attributes in the given event log:

1. case:citizen - This attribute indicates whether a tenant is a local resident or not. Differentiating between citizens and non-citizens can potentially result in discrimination or bias.
2. case:gender - This attribute shows the gender of a tenant. Different treatment based on gender is a common form of discrimination.
3. case:german-speaking - Even though it doesn't explicitly refer to race or ethnicity, this attribute can imply demographic information that could indirectly reveal sensitive racial or ethnic information. Distinguishing between German-speaking and non-German speaking tenants can cause issues in the analysis if the data shows disparities based on this characteristic.

These attributes should be carefully considered while analyzing the event logs to ensure fairness in the results and automated decision-making. If possible, it is recommended to avoid using these sensitive attributes in the analysis. Anonymization techniques could also be applied to protect individual privacy.