Based on the provided attribute names and descriptions, the following attributes could be considered sensitive for fairness:

* `case:citizen`: This attribute indicates whether a person is a citizen or not, which could be a sensitive attribute for fairness, especially in the context of housing rental.
* `case:gender`: This attribute specifies the gender of the individual, which is a protected attribute in many fairness contexts.
* `case:german speaking`: This attribute indicates whether a person speaks German or not, which could be a sensitive attribute for fairness, especially in regions where language is a protected characteristic.

These attributes could potentially be used to discriminate against certain groups of people, and therefore, fairness measures should be taken into account when analyzing or modeling this data.

It's worth noting that the `case:married` attribute might also be considered sensitive in certain contexts, but in the context of housing rental, it might be less relevant compared to the other attributes mentioned above.

The other attributes, such as `resource`, `start_timestamp`, `time`, and `time:timestamp`, seem to be more related to the process or administrative aspects of the rental process, and are less likely to be sensitive for fairness.